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Glama

Pubmed

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing important behavioral semantics: the meaning of each verdict, the special 'could_not_verify' case (check did not happen, not evidence), the 'unsupported' meaning, and the presence of verification_error{stage,detail}. It also reveals the internal routing logic between SEC/XBRL and the grounded pipeline, which is critical for callers to interpret results correctly.

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 rich but every sentence earns its place: paraphrase triggers, use instruction, pipeline explanation, return summary, caller-critical caveats, and efficiency note. It is well-structured with clear sections, and although lengthy, its density of actionable information justifies the length.

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?

Despite having no output schema, the description covers the return value structure (verdict, actual value with citation, reasoning) and the critical edge cases (could_not_verify, unsupported) that an agent must understand to use the result properly. It also explains both processing paths and the efficiency advantage, making it fully complete for a tool of this complexity.

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?

The input schema already provides thorough descriptions for both parameters, including examples for 'claim' and detailed guidance for 'tolerance_pct' (range, default, and use for hallucination detection). The description adds minimal additional semantics beyond the 'exact percent-delta math' mention, but since schema coverage is 100%, the baseline of 3 is appropriate.

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 explicit natural-language paraphrase examples and states the core function: fact-checking claims against authoritative sources. It clearly distinguishes itself from generic research/query tools by specifying the two internal pipelines (SEC EDGAR for company financials, grounded pipeline for everything else) and by noting it replaces multiple sequential calls.

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

Usage Guidelines4/5

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

It explicitly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies scope by distinguishing company-financial claims from any other factual claim. However, it does not explicitly name alternative tools or when-not-to-use scenarios, though the coverage of all factual claims makes that less necessary.

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.

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