Fetches PDFs and metadata from arXiv for scientific papers.
Resolves metadata and fetches open-access PDFs using Semantic Scholar's index.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@acatome-quest-mcp submit paper with DOI 10.1021/jacs.2c01234"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Paper-request MCP for scientific papers. The missing piece between
precis-mcp (navigates what's
already in your library) and
acatome-extract (ingests
PDFs that land in an inbox).
An LLM says "I want this paper" (DOI, arXiv id, title, or free-form citation). Quest:
Checks the store first — no duplicate work if we already have it.
Resolves the metadata via Crossref + Semantic Scholar + arXiv.
Flags misconceptions — broken DOI, DOI↔title mismatch, duplicate of an existing slug, fabrication suspect.
Fetches the PDF from legitimate open-access sources only and drops it
into the existing watch inbox, where acatome-extract takes over.
Returns a request id in milliseconds. Slow extraction happens out of band; the MCP call never blocks.
Quest fetches from arXiv, Unpaywall, OpenAlex, Europe PMC, and Semantic
Scholar's open-access index only. It does not, will not, and cannot be
configured to use Sci-Hub, LibGen, institutional proxies without explicit opt-
in, or any other paywall-circumvention mechanism. Failed retrievals yield a
needs_user status with the publisher URL, for you to retrieve manually.
Related MCP server: paper-fetch-mcp
pip install acatome-quest-mcp
# or with uv
uv add acatome-quest-mcpFor dedup against a local acatome-store:
pip install 'acatome-quest-mcp[store]'Tool | What it does |
| Resolve + optionally queue. Idempotent. |
| Read one or many requests. |
| Mutate. Modes: |
| Attach a user-supplied PDF (e.g. Discord attachment) to an existing request or create a new one, flip to |
submit(ref={"doi": "10.1021/jacs.2c01234"})
submit(ref={"title": "Anion exchange membranes for NOx reduction",
"authors": ["Feng, Z."], "year": 2024})
submit(ref={"raw": "Feng et al. 2024, Adv. Funct. Mater. 34, 2300512"})
submit(ref={"doi": "10.1234/x"}, dry_run=True) # resolve only, no queue
submit(ref={"doi": "10.1234/x"},
source={"document": "ch02.tex", "line": 147})Response:
{
"id": "9f3b…",
"status": "found_in_store",
"resolved": {"doi": "10.1021/jacs.2c01234",
"title": "…", "authors": ["…"], "year": 2024,
"ref": "smith2022jacs"},
"candidates": [],
"misconceptions": []
}status(id="9f3b…")
status(filter={"status": "needs_user"})
status(filter={"created_by": "asa", "has_misconception": True})
status(filter={"source_document": "ch02.tex"})update(id, mode="confirm", choice=0) # pick candidates[0]
update(id, mode="repoint", doi="10.1023/A:…") # user-corrected DOI
update(id, mode="flag", code="retracted",
evidence="Retraction Watch 2024-08-12")
update(id, mode="priority", priority=5)
update(id, mode="cancel")# User drops a PDF for an already-tracked request (reopens failed / needs_user):
submit_file(url="https://cdn.discordapp.com/…/paper.pdf",
request_id="7f3a…",
filename="feng2024.pdf")
# User supplies both a PDF and a DOI in one step (creates the request):
submit_file(url="https://cdn.discordapp.com/…/paper.pdf",
ref={"doi": "10.1021/jacs.2c01234"},
created_by="asa")
# Bytes already in memory (no URL to fetch):
submit_file(content_base64="JVBERi0xLjQKJf…",
request_id="7f3a…")PDF magic bytes are validated; HTML error pages are rejected. The file is written to the extractor's inbox (~/.acatome/inbox/ by default) and the request flips to ingesting. If the paper's DOI is already in the store, the tool short-circuits to found_in_store without writing anything.
The acatome-quest binary exposes the same surface as the MCP plus a couple of
shell-friendly helpers:
acatome-quest submit 10.1021/jacs.2c01234
acatome-quest status <id>
acatome-quest status --filter status=needs_user
acatome-quest status --filter status=needs_user --count # just prints "3"
acatome-quest update <id> repoint --doi 10.1023/A:…
acatome-quest submit-file --path ./feng2024.pdf --request-id 7f3a…
acatome-quest submit-file --url https://.../paper.pdf --doi 10.1021/jacs.2c01234
acatome-quest report # markdown worklist
acatome-quest report --document ch04.tex --format markdown # scoped
acatome-quest runner [--once]
acatome-quest reconcilereport renders a paste-ready markdown document for every request in
needs_user, failed, or extract_failed — each entry with citation,
DOI/arXiv link, failure reason, misconception evidence, and a concrete
suggested action (repoint DOI, drop PDF into ~/.acatome/inbox/, request via
interlibrary loan, …). Hand it to a librarian or paste into an ILL form.
Status | Meaning |
| Accepted, not yet fetched |
| Metadata lookup in progress (transient) |
| Dedup hit — slug in |
| Disambiguation or manual fetch required |
| Runner has claimed and is downloading |
| PDF in inbox, waiting for |
| Extraction done, slug in |
| PDF delivered but extraction failed |
| All sources exhausted |
|
|
Code | Severity | Trigger |
| major | Crossref 404 or syntactically malformed |
| major | 404, but |
| critical | DOI resolves but title fuzz < 60 vs request |
| critical | No S2/Crossref hit (fabrication suspect) |
| minor | Already in store under another slug |
| critical | S2 / Retraction Watch flag |
| info | arXiv preprint of a later journal paper |
| critical | User-dropped PDF resolved to a different paper than the request it was attached to |
agent ──submit()──► acatome-quest-mcp (FastMCP, stdio)
│
▼
cluster.papers.requests (Postgres)
│
▼
acatome-quest-runner (launchd, poll 30 s)
│
fetch: arxiv → unpaywall → …
│
▼
~/.acatome/inbox/<slug>__<hash>.pdf
│
▼
acatome-extract watch → acatome-store
▲
└── runner polls by DOI, flips to `ingested`Env var | Default | Description |
|
| Postgres DSN |
|
| Schema name for the |
|
| Drop directory watched by |
| (required at runner start) | Polite-pool contact |
| (recommended) | Crossref polite pool |
| (optional) | Raises S2 rate limit |
|
| Runner tick seconds |
|
| Max parallel fetches |
|
| Seconds to wait for ingest after PDF drop |
|
| Per- |
uv sync
uv run pytest
uv run ruff check .
uv run mypy src testsGPL-3.0-or-later. See LICENSE.
Read one or many paper requests.
Args:
id: A request id (uuid string). If given, filter is ignored.
filter: Narrow the list of rows returned. Supported keys:
status, created_by, has_misconception (bool),
source_document, limit.
Returns:
A single card (when id is set) or a list of cards.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| filter | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool returns a single card or list, but does not mention side effects, authentication, or other behavioral traits. For a read tool, this is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a clear structure using 'Args' and 'Returns' sections. Every sentence adds value with no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity and existence of an output schema, the description covers the basics: input parameters and return types. Missing details like error handling or pagination, but these are not critical for this simple read tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0%, so the description fully documents both parameters: 'id' as a UUID string and 'filter' with supported keys. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Read one or many paper requests,' which clearly indicates a read operation on a specific resource. This distinguishes it from sibling tools like 'submit' and 'update', which are write operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains when to use 'id' versus 'filter' and that 'id' overrides 'filter'. However, it does not explicitly mention when not to use this tool or direct users to alternatives for write operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a paper request. Resolves metadata, checks dedup, queues for fetch.
Args:
ref: The paper identifier. Any subset of:
{doi, arxiv, pmid, title, authors, year, raw}.
raw is a free-form citation string — Quest will try to extract
a DOI or arXiv id from it.
dry_run: If True, resolve and return candidates but do not persist.
source: Provenance — e.g. {"document": "ch02.tex", "line": 147}.
priority: Higher = served first by the runner. Default 0.
created_by: Agent slug or user id. Used for per-agent rate-limiting.
Returns:
The full request record, including resolved metadata, candidates, and
misconceptions. Status will be one of:
found_in_store (already have it), queued (waiting for runner),
needs_user (disambiguation or bad DOI required manual action).
Idempotent: calling submit twice with the same DOI while the first request is still open returns the same request id.
Do not fabricate quotes from a paper whose status is not ingested or
found_in_store.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| dry_run | No | ||
| source | No | ||
| priority | No | ||
| created_by | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully discloses behavior: resolution, dedup, queuing, idempotency, statuses, and per-agent rate-limiting. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with Args and Returns sections. Concise yet comprehensive; every sentence provides value. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all parameters and return values, including idempotency and statuses. Output schema exists but description still adds context. Complete for tool complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, description thoroughly explains each parameter: ref (subsets, raw), dry_run, source, priority, created_by. Adds significant meaning beyond bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Submit a paper request' and explains the process (resolves metadata, checks dedup, queues). Distinguishes from siblings like submit_file by focusing on reference-based submission.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides implicit guidelines through dry_run parameter for testing and priority/created_by for rate-limiting. Also warns not to fabricate quotes from non-ingested papers. Lacks explicit comparison to siblings but still informative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Attach a user-supplied PDF to a paper request.
Use this when a user drops a PDF (e.g. a Discord attachment) for a paper that Quest could not fetch automatically, or to pre-load a PDF you already have on disk.
Args:
url: A direct HTTP(S) link to the PDF. Must resolve to a fresh file
(follow-redirects is enabled, so short-lived Discord CDN URLs
work). Mutually exclusive with content_base64.
content_base64: Base64-encoded PDF bytes, for agents that already
have the file in memory. Prefer url when available so the
provenance URL is recorded on the request.
filename: Optional filename hint, used when naming the file written
to the inbox. Falls back to the request's author/year.
request_id: Attach to an existing request (preferred). Reopens
failed, extract_failed, or needs_user requests.
Refuses to overwrite already-closed or cancelled requests.
ref: Create a new request from this reference (same shape as
:func:submit) and attach the PDF. Use this when the paper
isn't already being tracked.
created_by: Agent or user id, used when ref is given.
Returns:
The full request record, flipped to ingesting. The background
runner reconciles with acatome-store once acatome-extract
has done its work.
Exactly one of url / content_base64 is required, and exactly
one of request_id / ref.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| content_base64 | No | ||
| filename | No | ||
| request_id | No | ||
| ref | No | ||
| created_by | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It discloses behavioral traits such as re-opening specific states ('failed', 'extract_failed', 'needs_user'), refusing to overwrite closed/cancelled requests, and the return value being the request record flipped to 'ingesting'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a concise intro, 'Args' section, 'Returns' section, and a final constraint note. Every sentence adds value, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters, no annotations, and an output schema, the description is complete. It explains parameter usage, return value, and even the background process. No critical information is missing for an AI to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides detailed semantics for each parameter, including mutual exclusivity (url vs content_base64, request_id vs ref), fallback behavior for filename, and usage of created_by. This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Attach a user-supplied PDF to a paper request.' It provides specific use cases (user-dropped PDF or pre-loaded file) and distinguishes from siblings by focusing on PDF attachment, not request creation or status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool (e.g., 'when a user drops a PDF...') and explains the two parameter groups (url vs content_base64, request_id vs ref). It lacks explicit when-not-to-use guidance but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Mutate a request. Single verb with an explicit mode (see
precis.put for the same pattern).
Modes:
confirm: pick one of candidates by index. Requires choice.
repoint: replace the DOI (user-corrected) and re-resolve. Requires doi.
flag: attach a misconception. Requires code; optional severity, evidence.
priority: change the runner priority. Requires priority.
cancel: terminate the request.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| mode | Yes | ||
| choice | No | ||
| doi | No | ||
| code | No | ||
| severity | No | ||
| evidence | No | ||
| priority | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavioral traits. It mentions the tool mutates a request (write operation), but does not address destructive effects (e.g., cancel), authorization needs, rate limits, or side effects. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and uses bullet points for modes, making it easy to scan. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers mode-specific parameters adequately but lacks information about return values (despite having an output schema), error conditions, or safety warnings. For a tool with 8 parameters and 5 modes, more high-level guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining which parameters are required for each mode (e.g., choice for confirm, doi for repoint). However, the id parameter is not described, leaving ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Mutate a request' and enumerates specific modes (confirm, repoint, flag, priority, cancel), each with a distinct purpose. This distinguishes it from sibling tools (status, submit, submit_file) which handle different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by detailing each mode and required parameters, but does not explicitly state when to use this tool versus alternatives. It provides clear context for usage but lacks exclusion guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.3.0statussubmitsubmit_fileupdateEach tool has a clearly distinct purpose: status for reading, submit for creating, submit_file for attaching PDFs, and update for modifying requests. No overlap exists, and descriptions are detailed enough to prevent confusion.
Names are lowercase and follow a simple pattern: three verbs (status, submit, update) and one compound verb_noun (submit_file). While not perfectly uniform, the naming is clear and predictable with minor deviations.
With 4 tools covering the core operations (create, read, update, attach file), the count is appropriate for the server's focused scope. It is slightly on the lower side but not insufficient.
The tool set covers the main lifecycle of paper requests: submission, status query, file attachment, and various update modes (including cancel). A minor gap is the lack of an explicit delete tool, but cancel effectively serves that purpose.
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