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Glama

Google_search_console

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

A4.9/5.0
Behavior5/5

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

Despite annotations already marking readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds crucial behavioral nuance: it explains the meaning of 'could_not_verify' (check did not happen, carries verification_error, must NOT be shown as evidence) versus 'unsupported' (no source covers it). This is exactly the kind of edge-case behavior that would be invisible without explicit disclosure and prevents a caller from misusing the result.

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 dense yet well-structured, with an opening trigger list, a clear use-case sentence, a pipeline overview with routing rules, a verdict list, and a critical caller warning. Every sentence adds operational value; the IMPORTANT section is appropriately emphasized. Length is justified by 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?

The tool handles two distinct verification pipelines, multiple verdict classifications, and has ambiguous failure modes. The description covers routing, evidence citation format, verdict semantics, and the critical could_not_verify vs unsupported distinction. With a rich input schema and clear annotations, there are no major gaps that would leave an agent guessing about return behavior or usage constraints.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds value by explaining what the default tolerance behavior is ('implied by wording, capped at 5') and when to override it (hallucination detection with 1–2). It also orients the 'claim' parameter with examples in the schema, so the description's added practical guidance is meaningful but not exhaustive.

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 multiple natural-language trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and states a specific verb+resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes this from sibling research tools by focusing on verifying a claim's truthfulness, not general research or entity lookup.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates two paths — structured SEC EDGAR for company-financial claims and a grounded pipeline for any other factual claim — and notes that it 'replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).' This gives the agent clear when-to-use and what-alternatives-it-replaces context.

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
Disambiguation3/5

Most tools have distinct purposes within their own clusters, and the GSC tools are clearly separated. However, ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and the several Polymarket tools (arbitrage, edges, fill_risk, kalshi_spread) can be confused without reading each description closely.

Naming Consistency3/5

Names are broadly snake_case and readable, with some useful prefixes (gsc_, polymarket_, pipeworx_). But conventions are mixed: some are verb_noun (list_subscriptions, resolve_entity), some are noun_compound (bet_research, entity_profile), and some are bare verbs (forget, recall), which prevents a predictable pattern.

Tool Count2/5

35 tools is heavy for any single server, but it is especially problematic given the server is named Google_search_console while only 4 of the 35 tools actually relate to Search Console. The rest form a sprawling Pipeworx/Polymarket research toolkit that would be far more coherent as its own server.

Completeness2/5

Relative to the stated Google Search Console purpose, the set is missing common operations such as submitting/removing sitemaps, requesting indexing, or managing URL inspections. The unrelated Pipeworx tools are extensive in their own domains but do not fill these gaps, leaving the GSC surface incomplete.

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