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Glama

Bookbrainz

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?

Despite annotations already indicating read-only, open-world, and idempotent behavior, the description adds substantial value: it discloses the source routing logic (SEC EDGAR+XBRL vs grounded pipeline), the expected return format (verdict, actual value with citation, reasoning), and critically clarifies the distinction between 'could_not_verify' (a failure, not evidence) and 'unsupported' (no source found). This is far beyond the annotation surface.

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 long and detailed, but every section earns its place: invocation phrases, routing logic, return semantics, and special verdict meanings. It is front-loaded with the natural-language patterns. Minor redundancy exists (e.g., the 'grounded or structured' phrasing repeats earlier pathway details), but overall it is efficiently structured.

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?

For a tool with two parameters and no output schema, the description fully compensates: it explains the verdict vocabulary, the difference between failure and unsupported, the two processing modes, and the citation format. It even warns callers not to treat 'could_not_verify' as evidence. This is complete enough for an agent to decide when to use it and to correctly interpret results.

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?

The input schema already documents both parameters (claim and tolerance_pct) with high coverage. The description adds meaningful guidance on tolerance_pct, explaining its default behavior (capped at 5% implied by wording) and recommending 1–2% for hallucination detection. It also provides concrete example claim formats, which helps the agent construct valid input.

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 this is a natural-language claim verification tool ('fact check', 'verify the claim that…') and specifies the two distinct processing paths (SEC EDGAR/XBRL fast path for company-financial claims, grounded pipeline for others). This distinguishes it from sibling tools like ask_pipeworx or search, which are general-purpose lookup/answer tools.

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,' providing a clear trigger condition. It also explains the internal branching based on claim type. However, it does not explicitly name alternatives or state when NOT to use this tool, so it falls short of a full 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

A3.8/5.0
Disambiguation2/5

The tool set mixes three distinct domains: BookBrainz entity access (browse, lookup, search), Pipeworx data routing (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and Polymarket betting analysis. Multiple tools have overlapping purposes, particularly the ask_pipeworx variants and the various polymarket_* tools, which an agent could easily confuse.

Naming Consistency4/5

All tool names follow a readable snake_case convention. While some are single verbs (browse, lookup, search), others use compound patterns (ask_pipeworx_grounded, polymarket_edge_tracker, pipeworx_trending), and there are a few noun-first names (entity_profile, pipeworx_feedback, recent_changes). The inconsistency is minor and does not hinder readability.

Tool Count2/5

With 34 tools, this is a large surface for a server named Bookbrainz, yet only 3 tools (browse, lookup, search) actually serve that domain. The remaining 31 tools are unrelated Pipeworx/Polymarket functionality, making the tool count inappropriate for the stated server purpose and suggesting a mislabeled or overloaded server.

Completeness2/5

The BookBrainz domain is severely incomplete: it offers read-only access (search, browse, lookup) but no create, update, or delete operations for entities. For the broader Pipeworx/Polymarket functionality, coverage is quite deep, but that is not the server's stated domain. This leaves a fundamental gap for the BookBrainz use case.

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