Skills asta-skill

Use when searching academic papers via Ai2 Asta (Semantic Scholar corpus) through the Asta MCP server. Triggers on academic search, paper lookup, citation traversal, author search, or snippet search when the Asta MCP server is configured. Works with Claude Code, Codex, Hermes, OpenClaw, Windsurf, Cursor, and any MCP-compatible agent.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/agents365-ai/asta-skill" ~/.claude/skills/clawdbot-skills-asta-skill && rm -rf "$T"

manifest: skills/agents365-ai/asta-skill/SKILL.md

source content

Asta MCP — Academic Paper Search

Asta is Ai2's Scientific Corpus Tool, exposing the Semantic Scholar academic graph over MCP (streamable HTTP transport). This skill tells agents which Asta tool to call for which intent, and how to compose them into useful workflows.

MCP endpoint:
```
https://asta-tools.allen.ai/mcp/v1
```
Auth:
```
x-api-key
```
header (request key at https://share.hsforms.com/1L4hUh20oT3mu8iXJQMV77w3ioxm)
Transport: streamable HTTP

Prerequisite Check

Before invoking any tool, verify the Asta MCP server is registered in the host agent. Tool names will be prefixed by the MCP server name chosen at install time (commonly

asta__<tool>

mcp__asta__<tool>

). If no Asta tools are visible, direct the user to the Installation section below.

Tool Map — Intent → Asta Tool

User intent	Asta tool	Notes
Broad topic search	`search_papers_by_relevance`	Supports venue + date filters
Known paper title	`search_paper_by_title`	Optional venue restriction
Known DOI / arXiv / PMID / CorpusId / MAG / ACL / SHA / URL	`get_paper`	Single-paper lookup
Multiple known IDs at once	`get_paper_batch`	Batch lookup — prefer over N sequential `get_paper` calls
Who cited paper X	`get_citations`	Citation traversal with filters, paginated
Find author by name	`search_authors_by_name`	Returns profile info
An author's publications	`get_author_papers`	Pass author id from previous call
Find passages mentioning X	`snippet_search`	~500-word excerpts from paper bodies

All tools accept date-range filters and field selection — pass them whenever the user's intent constrains scope (e.g., "recent", "since 2022", "at NeurIPS").

⚠️

fields

parameter — avoid context blowups

get_paper

get_paper_batch

accept a

fields

string. Never request
citations
or
references
via

fields

— a single highly-cited paper (e.g. Attention Is All You Need) returns 200k+ characters and will overflow the agent's context window. Use the dedicated

get_citations

tool instead (it paginates).

Safe default

fields

for

get_paper

title,year,authors,venue,tldr,url,abstract

Add

journal

publicationDate

fieldsOfStudy

isOpenAccess

only when needed.

Workflow Patterns

Pattern 1 — Topic Discovery

search_papers_by_relevance(query, year="2022-", venue=?)

→ initial hits

Rank/present top N by citationCount + recency
Offer follow-ups:
```
get_citations
```
on the most influential, or
```
snippet_search
```
for specific claims

Pattern 2 — Seed-Paper Expansion

```
get_paper(DOI|arXiv|...)
```
→ verify seed
```
get_citations(paperId)
```
→ forward expansion
Optionally
```
search_papers_by_relevance
```
with seed title terms for sideways discovery
Deduplicate by paperId before presenting

Pattern 3 — Author Deep-Dive

```
search_authors_by_name(name)
```
→ pick correct profile (disambiguate by affiliation)
```
get_author_papers(authorId)
```
→ full publication list
Filter client-side by topic keywords or date

Pattern 4 — Evidence Retrieval

```
snippet_search(claim_query)
```
→ find passages making/supporting a claim
For each hit, optionally
```
get_paper(id)
```
for full metadata

Output & Interaction Rules

Always report total count and which tool was used.
Present top 10 as a table (title, year, venue, citations), then details for the most relevant.
If the user writes in Chinese, present summaries in Chinese; keep titles in original language.
After results, offer: Details / Refine / Citations / Snippet / Export / Done.

Critical Rules

Prefer batched intent over ping-pong. If the user's question needs two independent lookups, issue them as parallel MCP tool calls in one turn, not sequentially.
Never guess IDs. If a user gives a fuzzy title, use
```
search_paper_by_title
```
before
```
get_paper
```
.
Respect rate limits. An API key buys higher limits but not unlimited — stop expanding citation graphs beyond what the user asked for.
Do not fabricate fields. If Asta returns null
```
abstract
```
or
```
venue
```
, say so rather than inventing.

Relationship to

semanticscholar-skill

Both wrap the Semantic Scholar corpus, but target different runtimes:

	`semanticscholar-skill`	`asta-skill`
Transport	Python + direct REST ( `s2.py` )	MCP (streamable HTTP)
Host needs	`S2_API_KEY` + Python	Asta MCP registered in host
Best for	Scripted batch workflows, custom filters	Zero-code agent integration (Claude Code, Codex, Cursor, Windsurf, OpenClaw)
Auth	`S2_API_KEY`	`ASTA_API_KEY` via `x-api-key` header

Use

asta-skill

when the host agent supports MCP; fall back to

semanticscholar-skill

for scripted/pipeline work.

Installation

Set

ASTA_API_KEY

in your shell first:

export ASTA_API_KEY="..."   # request at https://share.hsforms.com/1L4hUh20oT3mu8iXJQMV77w3ioxm

Claude Code

claude mcp add asta \
  --transport http \
  --url https://asta-tools.allen.ai/mcp/v1 \
  --header "x-api-key: $ASTA_API_KEY"

Or edit

~/.claude.json

.mcp.json

{
  "mcpServers": {
    "asta": {
      "type": "http",
      "url": "https://asta-tools.allen.ai/mcp/v1",
      "headers": { "x-api-key": "${ASTA_API_KEY}" }
    }
  }
}

Codex CLI

Edit

~/.codex/config.toml

[mcp_servers.asta]
type = "http"
url = "https://asta-tools.allen.ai/mcp/v1"
headers = { "x-api-key" = "${ASTA_API_KEY}" }

Windsurf / Cursor / Hermes / other MCP clients

Add to the client's MCP server config file:

{
  "mcpServers": {
    "asta": {
      "serverUrl": "https://asta-tools.allen.ai/mcp/v1",
      "headers": { "x-api-key": "<YOUR_API_KEY>" }
    }
  }
}

LM Studio

LM Studio 0.3.17+ supports remote MCP servers. Edit

~/.lmstudio/mcp.json

(macOS/Linux) or

%USERPROFILE%\.lmstudio\mcp.json

(Windows) — or in the app: Program tab → Install > Edit mcp.json:

{
  "mcpServers": {
    "asta": {
      "url": "https://asta-tools.allen.ai/mcp/v1",
      "headers": { "x-api-key": "<YOUR_API_KEY>" }
    }
  }
}

Only models with "Tool Use: Supported" in LM Studio's model loader will be able to call Asta tools. Recommended: Qwen 2.5 / 3 Instruct (7B+), Llama 3.1 / 3.3 Instruct (8B+), Mistral / Mixtral Instruct.

OpenClaw

Install this skill into

~/.openclaw/skills/asta-skill/

and register the MCP server in your OpenClaw config using the same URL +

x-api-key

header pattern. The skill's frontmatter declares

ASTA_API_KEY

as required via

metadata.openclaw.requires.env

Verification

After installation, ask the agent: "Use Asta to look up the paper with DOI 10.48550/arXiv.1706.03762." A successful call returns the "Attention Is All You Need" paper metadata. If the agent reports no Asta tools, the MCP server is not registered — re-check the config file path and restart the host.

Troubleshooting

Symptom	Cause	Fix
`401 Unauthorized`	Missing or invalid `x-api-key`	Verify `ASTA_API_KEY` is set and header is forwarded
`429 Too Many Requests`	Rate limit hit	Slow down / batch; ensure API key is attached (unauth'd limits are lower)
No Asta tools visible	MCP server not registered in host	Re-run install step, restart agent
Empty `abstract`	Not all corpus papers have full text	Use `snippet_search` instead, or fall back to title + TLDR
Author disambiguation wrong	Common name collisions	Inspect affiliations in `search_authors_by_name` before calling `get_author_papers`