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Glama

Pdbe

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. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (read-only, open-world, idempotent), the description discloses critical behavioral nuances: the dual pipeline (structured SEC EDGAR vs. grounded fallback), the exact verdict vocabulary, and the vital distinction between 'could_not_verify' (check failed) and 'unsupported' (no source coverage). It also warns callers not to treat 'could_not_verify' as evidence, adding significant value not present in 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 comprehensive yet efficiently structured: it front-loads with natural-language triggers, then explains usage, return value, and important exceptions. Every sentence earns its place, covering routing, verdicts, citations, and error handling without redundancy or fluff.

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?

There is no output schema, so the description must fully explain return values and behaviors. It does so thoroughly: verdict types, actual value with citation, reasoning, and the semantic distinction between 'could_not_verify' and 'unsupported'. It even notes the tool replaces multiple sequential calls, giving a full picture of the tool's role and expectations.

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 100% coverage with detailed descriptions for both 'claim' and 'tolerance_pct', including the override behavior and default cap. The description adds example claims but no new semantic information beyond what the schema states, so it does not elevate beyond the baseline for high schema coverage.

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 clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides example queries ('Is it true that…', 'fact check') and explicitly distinguishes it from general research tools by focusing on fact-checking user statements. The scope is specific and actionable.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct', giving a clear trigger condition. It also details the internal routing for company-financial vs. other claims, which explains when one path is preferred. However, it does not name alternative sibling tools or provide explicit 'when not to use' exclusions, so it falls just short of a 5.

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

B3.3/5.0
Disambiguation2/5

Many tools overlap in purpose: ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta, and deep_research all perform similar data lookups with slight variations. Prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) also overlap. Only the three PDBe-specific tools are clearly distinct, but overall the set is confusing.

Naming Consistency1/5

Tool names follow no consistent pattern: some are verb_noun (ask_pipeworx, get_molecules), others are noun_verb (ai_visibility_check), or have mixed conventions (generate_llms_txt, uniprot_mappings). The variety of verbs (ask, bet, compare, discover, generate, get, list, recall, remember) makes it hard to predict tool names.

Tool Count2/5

With 34 tools, the server is overstuffed for a single domain. It mixes PDBe-specific tools (3) with a large set of general-purpose data tools, prediction market tools, subscriptions, and memory tools. This bloat suggests the server should be split into focused services.

Completeness1/5

For the stated PDBe domain, only three tools exist (get_molecules, get_summary, uniprot_mappings), missing essential operations like search, download, or advanced queries. The server's overall purpose is unclear, and it feels like a random collection of tools rather than a coherent API.

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