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Glama

Data Rennes

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

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive, but the description adds crucial behavioral context: it explains the verdict taxonomy, distinguishes between could_not_verify and unsupported with a warning that the former must not be treated as evidence, and discloses the internal two-path routing (structured SEC path vs. grounded pipeline). The verification_error{stage,detail} detail further exposes failure modes. 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.

Conciseness4/5

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

The description is dense but appropriately detailed for a tool with multiple routing paths and nuanced result semantics. It front-loads trigger phrases and purpose, then handles edge cases and warnings. While a bit long, every sentence adds value—especially the caller-important warning about could_not_verify. It earns a 4 rather than 5 because the traffic of examples and multiple clauses could be tightened without losing meaning.

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?

Given the tool's complexity and the absence of an output schema, the description is exceptionally complete: it specifies return values (verdict categories, actual value, citation, reasoning), explains failure states, gives concrete examples for financial and non-financial claims, and indicates the tool replaces multiple sequential calls. This gives an agent everything needed to select and invoke the tool appropriately.

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 covers 100% of parameters with rich descriptions, so the baseline is 3. The main description does not add additional parameter semantics beyond the schema; it focuses on usage and return values. The schema itself already explains tolerance_pct overrides and hallucination-detection usage, so the description does not need to compensate, but it also doesn't elevate the score.

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 concrete trigger phrases ("Is it true that…", "fact check", "verify the claim that…") and immediately defines the tool as "natural-language claim verification against authoritative sources." It clearly distinguishes itself from general-purpose siblings like ask_pipeworx or deep_research by focusing on claims requiring a verdict, and further differentiates between company-financial verification (SEC EDGAR/XBRL) and any other factual claim via a grounded pipeline.

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?

Explicit guidance is given: "Use whenever the agent needs to check whether something a user said is factually correct." It also details routing rules for company-financial vs. other claims, clarifies when a result means the check did not happen (could_not_verify) versus unsupported, and notes that this tool replaces 4–6 sequential calls, directly positioning it as the preferred single-call alternative.

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

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and several prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). While descriptions attempt to differentiate, an agent could easily select the wrong tool.

Naming Consistency3/5

Tool names use a mix of verb-initial (ask_pipeworx, compare_entities) and noun-phrase patterns (entity_profile, dataset_info), with no consistent verb_noun structure. Naming is readable but lacks a predictable pattern.

Tool Count3/5

With 34 tools, the server is on the heavy side. The broad scope (data querying, prediction markets, company research, local open data) somewhat justifies the count, but several tools could be consolidated (e.g., the various polymarket tools).

Completeness4/5

The tool set covers a wide range of functionalities including data querying, company research, prediction market analysis, and memory management. Minor gaps exist (e.g., no user authentication tools beyond subscriptions), but the surface is largely comprehensive for its intended purpose.

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