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Glama

Inegi

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?

Beyond readOnly/idempotent annotations, the description discloses the full verdict set, the critical distinction between could_not_verify and unsupported, the structured vs grounded pipeline, and the error field. The 'IMPORTANT for callers' note clarifies that could_not_verify is not evidence, which is essential behavioral context.

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 long but every sentence contributes: it front-loads example phrasings, explains routing, defines return values, and provides error handling. No filler or repetition—each part earns its place given the tool's complexity.

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?

With no output schema, the description compensates by explaining the verdict values, the format of the actual value and citation, and the nuanced meaning of could_not_verify and unsupported. It also covers the fallback pipeline and the reasoning component, making the tool's behavior fully understandable.

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

Parameters5/5

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

While schema coverage is 100%, the description enriches both parameters: it explains tolerance_pct overrides the implied tolerance, sets 1–2 for hallucination detection, and gives the default cap. The claim parameter is illustrated with concrete examples, adding semantic depth beyond the schema.

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

Purpose4/5

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

The description clearly defines the tool as natural-language claim verification against authoritative sources, with example phrasings and a specific verb-resource pairing. It distinguishes coverage for company-financial vs other claims but does not explicitly name sibling alternatives for comparison.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear context. It also details the two-path routing (SEC EDGAR/XBRL vs grounded pipeline) and mentions replacing sequential calls, but does not explicitly state when not to use or name alternative tools.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there are some close groups: the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) require careful reading to distinguish subtle differences.

Naming Consistency3/5

Naming is inconsistent: some tools start with verbs (ask, compare, generate), others with nouns (inegi_indicator, entity_profile), and there is no uniform verb_noun pattern. However, within families, naming is consistent (e.g., ask_pipeworx*).

Tool Count4/5

With 33 tools, the server is on the higher side but not excessive. Each tool serves a distinct purpose, though some could be merged (e.g., ask_pipeworx variants). The count is justified by the breadth of domains covered (INEGI, Pipeworx, Polymarket, utilities).

Completeness4/5

The toolset covers a wide range of functionalities: Mexican demographic/economic data, general structured data queries, prediction market analysis, and utility tools. Minor gaps exist (e.g., limited direct API for some Mexican indicators), but overall the surface is comprehensive for the stated purpose.

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