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Glama

Ted Eu

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 the annotations (readOnly, openWorld, idempotent), the description reveals important behavior: the two pipeline paths, the meaning of each verdict, and a critical caveat that 'could_not_verify' must not be treated as evidence. It also discloses that unsupported means no source was covered, adding substantial context an agent needs to interpret results safely.

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 longer than typical descriptions, every sentence earns its place: trigger phrases, routing logic, return-value summary, and a prominent 'IMPORTANT' warning. It is structured with clear separators and scoping, making the density appropriate rather than redundant—a model of information-dense yet readable tool documentation.

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 enumerating every possible verdict and clarifying the nuanced difference between could_not_verify and unsupported. It also details both the structured and grounded paths, what a response contains (value, citation, reasoning), and the tolerance semantics. This gives an agent everything needed to invoke and interpret the tool correctly.

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 already provides 100% coverage with detailed examples for 'claim' and a full explanation of 'tolerance_pct' including range, override behavior, and default. The description adds conceptual context like 'exact percent-delta math' but does not significantly extend the parameter-level guidance beyond what the schema offers, so the baseline 3 applies.

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 and states the tool performs 'natural-language claim verification against authoritative sources'. It clearly distinguishes this from the sibling list by focusing on factual claim checking and even notes it replaces 4–6 sequential calls, making its role unambiguous.

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?

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides routing rules for company-financial vs. any other factual claim. This tells the agent exactly when to invoke it and how it will behave, preempting unnecessary alternatives.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose described in detail. Even tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by hallucination resistance, account requirements, and use cases. Polymarket tools are each specialized (arbitrage, edges, fill risk, etc.). No two tools appear to do the same thing.

Naming Consistency5/5

All tool names follow snake_case consistently. They use descriptive verb-noun patterns (e.g., ask_pipeworx, bet_research, compare_entities, subscribe). No mixing of conventions or ambiguous names.

Tool Count4/5

32 tools is on the higher side but justified by the server's broad scope covering company research, prediction markets, data lookups, memory, subscriptions, and more. Each tool serves a distinct function, though the count might feel slightly heavy for a single server.

Completeness4/5

The toolset covers core workflows for company analysis, prediction market operations, data retrieval, and system management (memory, subscriptions). Minor gaps exist (e.g., no direct tool for non-company entity profiles beyond drugs), but the overall surface is comprehensive for the intended multi-purpose assistant.

Resources