Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LuuOW/meridian-mcp --skill semantic-scholar-apigit clone --depth 1 https://github.com/LuuOW/meridian-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/luuow/meridian-mcp/semantic-scholar-api)<a href="https://agentmods.dev/skills/luuow/meridian-mcp/semantic-scholar-api"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/semantic-scholar-api/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/luuow/meridian-mcp/semantic-scholar-api"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/semantic-scholar-api.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.02186 |
| Opus 5 | $0.00043 | $0.01093 |
| Sonnet 5 | $0.00017 | $0.00437 |
| Haiku 4.5 | $0.00009 | $0.00219 |
Grade A, and why
semantic-scholar-api scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
r = requests.get(f"{BASE}/paper/{pid}", params={"fields": fields}, headers=HEADERS, timeout=15) How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
semantic-scholar-api
When to invoke
- User has a seed paper and wants its citation neighborhood
- Building a recommender that extends past keyword match
- Author disambiguation (two people with same name)
- Finding "influential" papers in a topic (citation-weighted)
- Getting canonical abstract/DOI when arXiv's metadata is incomplete
Two complementary APIs
| API | Strength | Rate limit (no key) |
|---|---|---|
Semantic Scholar api.semanticscholar.org/graph/v1/ |
Rich citation graph, influence scoring, AI-extracted tldr | 1 req/sec |
OpenAlex api.openalex.org/ |
Larger coverage (250M+ works), institution + funding data | 10 req/sec with email in User-Agent |
Use S2 for depth (citations, references, influence), OpenAlex for breadth (coverage, metadata richness).
Semantic Scholar — core endpoints
GET /graph/v1/paper/{id}
GET /graph/v1/paper/{id}/citations # who cites this paper
GET /graph/v1/paper/{id}/references # what this paper cites
GET /graph/v1/paper/search?query=...
GET /graph/v1/author/{id}/papers
Paper ID formats S2 accepts
- arXiv:
arXiv:2604.13012orARXIV:2604.13012 - DOI:
10.1038/s41586-023-06792-0 - PubMed:
PMID:34567890 - CorpusId:
CorpusId:12345678 - S2 SHA:
649def34f8be52c8b66281af98ae884c09aef38b
Minimal citation walk
import requests, time
BASE = "https://api.semanticscholar.org/graph/v1"
HEADERS = {"User-Agent": "research-tool/1.0 ([email protected])"}
def paper(pid, fields="title,abstract,tldr,year,citationCount,authors"):
r = requests.get(f"{BASE}/paper/{pid}", params={"fields": fields}, headers=HEADERS, timeout=15)
r.raise_for_status()
return r.json()
def citations(pid, limit=20, fields="title,year,citationCount,authors"):
r = requests.get(
f"{BASE}/paper/{pid}/citations",
params={"fields": fields, "limit": limit},
headers=HEADERS, timeout=15,
)
r.raise_for_status()
return [c["citingPaper"] for c in r.json().get("data", [])]
def references(pid, limit=20, fields="title,year,citationCount"):
r = requests.get(
f"{BASE}/paper/{pid}/references",
params={"fields": fields, "limit": limit},
headers=HEADERS, timeout=15,
)
r.raise_for_status()
return [c["citedPaper"] for c in r.json().get("data", [])]
# Example: walk 1 hop out from an arxiv paper
seed = paper("arXiv:2604.13032")
print(seed["title"], "→", seed["tldr"])
time.sleep(1) # rate limit
for citer in citations("arXiv:2604.13032", limit=5):
print(" cited by:", citer["title"])
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 186 lines · 85 tokens per session scan A 7c295cc68fa7
semantic-scholar-api is a skill published in the GitHub repository LuuOW/meridian-mcp (0 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 2,186 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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