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Glama

Pride

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, openWorld), the description discloses critical behavioral nuances: the difference between could_not_verify (inconclusive due to tool failure) and unsupported (no source found), the routing between structured and grounded pipelines, and that it returns a verdict with citations. It also clarifies that could_not_verify must not be shown as evidence, which is vital.

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?

Though long, every sentence serves a purpose: trigger phrases, routing logic, error semantics, and value proposition. The structure is logical and front-loaded with natural-language examples. No redundancy or filler.

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 description fully covers behavior, return values (verdict types, actual value, citation, reasoning), and edge cases (could_not_verify, unsupported). Given the absence of an output schema, the description compensates completely, making the tool's behavior predictable and safe to invoke.

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?

Schema coverage is 100% with detailed descriptions and examples for both `claim` and `tolerance_pct`. The description largely repeats this information (e.g., tolerance override) without adding new semantic meaning. It adds a usage example but no substantive parameter guidance beyond the schema, so a baseline 3 is appropriate.

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 specific trigger examples ("Is it true that…", "fact check") and distinguishes it from sibling tools by explaining it replaces 4–6 sequential calls.

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?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR fast path) and all other claims (grounded pipeline), and notes when it should not be used (e.g., could_not_verify means the check did not happen, not evidence).

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

A3.9/5.0
Disambiguation2/5

ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, creating a true duplicate. The six-tool Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) plus discover_tools vs suggest_questions give agents overlapping entry points that require deep reading to disambiguate.

Naming Consistency3/5

Sub-families are internally consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the server mixes verb_noun, domain_noun, and bare-verb styles across tools. bet_research breaks the polymarket_ prefix pattern, and ai_visibility_check vs scan_competitor_ai_presence use different words for the same underlying concept.

Tool Count2/5

33 tools is heavy and spans at least six unrelated domains: a data-gateway, prediction markets, key-value memory, subscription management, PRIDE proteomics, and standalone utilities (generate_llms_txt, scan_dependency). The scope is so broad that it feels like multiple servers merged into one, making the surface hard to navigate.

Completeness4/5

The dominant data-query domain is well covered: query, grounded query, deep research, profiles, comparison, change feeds, validation, entity resolution, and discovery are all present. Subscription and memory lifecycles are complete, and the prediction-market research surface is thorough; minor gaps exist only in peripheral areas like PRIDE project download/file details.

Resources