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Glama

Ashby

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly/not destructive, but the description goes far beyond by clearly explaining the compound nature (replaces 4–6 sequential calls), the specific verdict values, the critical distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source found), and the presence of verification_error details. This is valuable behavioral context beyond the structured annotations.

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 longer than average but every part earns its place: example phrasings, purpose, routing logic, return values, and caller warnings. It is well-structured and front-loaded with examples, though slightly wordy in the middle. It is appropriately sized given 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 is complex (two execution paths, multiple verdict types) and has no output schema, so the description must explain return values and edge cases. It does so thoroughly, including the meaning of each verdict, the citation format, and special handling for failures. This is complete enough for an agent to invoke the tool correctly and interpret results.

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

Parameters5/5

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

With 100% schema description coverage, the baseline is 3, but the description substantially enriches both parameters: it gives concrete examples for 'claim' and explains the behavior of 'tolerance_pct' (default implied by wording, capped at 5, overrides, and use cases for hallucination detection). This adds meaningful guidance beyond the schema.

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 states a specific verb ('verify') and resource ('natural-language factual claims against authoritative sources'), includes concrete example phrasings, and clearly distinguishes this tool from sibling research/comparison tools. It is unambiguous about what the tool does and returns a verdict.

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 to use it whenever the agent needs to check whether something a user said is factually correct. It also explains the two distinct routing paths (company-financial vs. other claims) but does not explicitly mention when NOT to use the tool or name alternative tools for non-verification tasks.

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
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants; polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. Research tools like entity_profile, compare_entities, recent_changes, and validate_claim also blur together, making it hard to pick the right tool.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use verb_noun (get_candidate, list_applications, remember), others use noun_verb (bet_research, entity_profile) or prefix-only patterns (ask_pipeworx, pipeworx_feedback, polymarket_edges). The Ashby tools use ashby_ prefix, but the rest mix pipeworx_, polymarket_, and bare names, with no uniform style.

Tool Count2/5

36 tools is excessive for a coherent server and spans unrelated domains: ATS (Ashby), data queries (Pipeworx), prediction markets (Polymarket), memory, subscriptions, and web utilities. This feels like a kitchen sink rather than a focused toolset, and the count alone makes selection overwhelming.

Completeness2/5

The Ashby ATS subset is incomplete: it provides get/list operations but no create, update, or delete for candidates or jobs, and no interview management. The broader server's scope is so mixed that each domain has obvious gaps, leaving agents unable to complete common workflows end-to-end.

Resources