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Glama

Pubmed

by io.github.pipeworx-io

Get Citations

get_citations
Read-onlyIdempotent

Find papers that CITE a given article — forward citation search. Pass one PMID; returns citing papers (most recent first) with full citation metadata. Use for "who cited this", "has this finding been replicated or challenged", or tracking a paper's downstream impact. NOTE: coverage is the PubMed Central citation graph (open-access + participating publishers), so the count is a FLOOR, not the paper's total citation count (for that, a tool like Semantic Scholar / OpenAlex covers more). Distinct from get_related_articles (similar papers, not citing papers).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pmidYesA single PubMed ID to find citing papers for (e.g., "24025838")
limitNoNumber of citing papers to return (1-50, default 10)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 15,
+    "pmid": "24025838"
+  }
+]
  2. Added

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even though readOnlyHint and openWorldHint are present, the description adds crucial context: it explains the PMC citation graph coverage limitation, that counts are a floor, and that results are ordered most recent first. This goes beyond the annotations and gives the agent a realistic expectation of output completeness. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, but every sentence earns its place: core action, usage, coverage caveat, and sibling differentiation. It is front-loaded with the main purpose and avoids filler. This is efficient, information-dense writing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a citation-search tool with no output schema, the description covers all critical aspects: what it does, how to use it, the important coverage limitation, alternatives, and distinction from a similar sibling. It even mentions output content ('full citation metadata'). The agent has enough context to invoke it correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for both parameters (pmid and limit), so the baseline is 3. The description only says 'Pass one PMID' which echoes the schema, and adds no deeper semantic meaning (e.g., format specifics or edge cases) beyond what the schema already provides. The schema itself is sufficiently descriptive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Find papers that CITE a given article — forward citation search.' It clearly distinguishes from the sibling tool get_related_articles by explicitly stating it returns citing papers, not similar papers. This is an unambiguous, well-scoped purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases ('who cited this', 'has this finding been replicated or challenged', 'tracking downstream impact') and explicitly names alternatives for broader citation coverage (Semantic Scholar/OpenAlex). It also contrasts with get_related_articles, giving clear when-to-use vs. when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation2/5

Several tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions against the same data sources, and the Polymarket suite (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) has unclear boundaries. An agent would struggle to pick the right one.

Naming Consistency2/5

Names mix verb phrases (search_pubmed, get_abstract, validate_claim) with noun phrases (entity_profile, polymarket_arbitrage, bet_research) and bare verbs (remember, forget). The ask_pipeworx family uses a non-standard prefix, and there's no consistent verb_noun pattern across the set.

Tool Count2/5

37 tools is far too many for a server that presents as a PubMed tool. The majority are unrelated to biomedical literature (memory, subscriptions, prediction markets, real estate, etc.), making the surface feel bloated and unfocused.

Completeness4/5

The PubMed-specific tools form a complete lifecycle: search, citation metadata, abstract, full text, forward citations, and related articles. However, the server's broader domain is unclear and unevenly covered — many non-PubMed areas have partial coverage.

Resources