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Codestats

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?

Annotations already declare read-only, open-world, idempotent, and non-destructive hints. The description adds crucial behavioral semantics: 'could_not_verify' means the check did not happen and carries a verification_error (not evidence for/against), while 'unsupported' means no source covers it. It also explains grounding, verbatim evidence, and judging behavior beyond the annotations.

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 earns its place: query examples, routing logic, return verdicts, citation format, error semantics, and the efficiency benefit over sequential calls. It is front-loaded with the key use case and contains no filler or repetition of schema fields.

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 the full set of verdicts, describing the returned value and citation, and clearly distinguishing 'could_not_verify' from 'unsupported'. It covers behavior for both company-financial and general claims, including failure modes, making it complete for a tool of this complexity.

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 covers both parameters at 100%, so baseline is 3. The description adds value by explaining tolerance_pct operational semantics: it overrides the tolerance implied by the claim wording, suggests 1–2 for hallucination detection, and notes the default is capped at 5. This goes beyond the schema's basic range description.

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 natural-language phrasings and explicitly names the tool's function as claim verification against authoritative sources. It clearly distinguishes this from sibling tools by focusing on fact-checking a user's claim and by describing a specific dual routing path for financial vs. other factual claims.

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' and outlines when the SEC EDGAR/XBRL fast path applies versus the grounded pipeline. It does not name sibling alternatives or state exclusions, but the context is strong enough to guide tool selection.

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

A3.5/5.0
Disambiguation3/5

Most tools have distinct, well-described purposes, but clusters like ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded and the five polymarket_* tools have overlapping scopes that could cause misselection. The beta currently behaves identically to the stable router, and `recent` vs `recent_changes` vs `recent_alerts` are confusingly similar names for different domains.

Naming Consistency2/5

All names use lowercase underscores, but there is no consistent verb_noun pattern: some are verbs (ask, compare, generate), some are nouns (entity_profile, recent, user), and some are adjectives (deep_research). There are coherent subfamilies (subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall naming is a mix of conventions.

Tool Count2/5

33 tools is well above the 25+ threshold, making the surface heavy and hard to navigate. Many are highly specialized meta-tools (e.g., five Polymarket analyzers) that could be consolidated.

Completeness2/5

For a server named 'Codestats', only `recent` and `user` address coding stats, leaving major gaps in what that name implies. The broader data/research/betting capabilities are fairly rich, but the server's stated identity is under-served and there's no clear lifecycle coverage for any single domain.

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