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Glama

flights

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

A4.5/5.0
Behavior5/5

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

Annotations (readOnly, idempotent) are supplemented with deep behavioral detail: the internal routing to SEC EDGAR vs grounded pipeline, the full verdict list, and a critical warning that 'could_not_verify' indicates a failed check (not evidence) while 'unsupported' means no source found. Goes far beyond what annotations provide.

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, it is efficiently organized: starts with trigger phrases and purpose, then usage, behavioral details, return values, an IMPORTANT caveat, and a note on efficiency. Every sentence earns its place, and the structure helps the agent quickly grasp key semantics.

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?

Compensates for the lack of an output schema by listing all possible verdicts, explaining the meaning of could_not_verify vs unsupported, describing the return value (with citation and reasoning), and clarifying the composite nature (replaces 4–6 calls). No critical information is missing for a complex tool.

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 description coverage is 100% for both parameters. The description adds context about tolerance override (e.g., 'set 1–2 for hallucination detection') but the schema already explains the parameters well, so the description provides marginal additional value.

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?

States a specific verb+resource+scope: 'natural-language claim verification against authoritative sources.' Trigger phrases ('fact check', 'verify the claim that…') make intent unmistakable. Distinct from sibling tools by describing two pathways (SEC EDGAR structured vs grounded pipeline), clearly differentiating it from general Q&A or research 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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and gives guidance for company-financial vs other claims. Lacks explicit when-not-to-use or named alternatives, but provides clear context and even notes it replaces 4–6 sequential calls, which is strong usage guidance.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research blur the query/research boundary, and the polymarket_* family plus bet_research all target prediction-market analysis. An agent would frequently struggle to pick the correct tool among these near-duplicates.

Naming Consistency3/5

All names are snake_case, but the verb/noun style is inconsistent: get_* for flight lookups, ask_* for queries, noun-style names like entity_profile and bet_research, and the polymarket_* prefix group. Some subgroups are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

36 tools is far too many for a server named 'flights' — only 5 tools actually relate to aviation, while the rest span prediction markets, SEC/FDA data, npm packages, memory, and feedback. Even viewed as a general data platform, the count is heavy and the scope is unfocused.

Completeness2/5

For a flights server, the surface is severely incomplete: no scheduled flight status, delays, cancellations, or airport schedules — only live ADS-B snapshots. For the broader data-research domain the tools imply, coverage is better but still scattered, with no coherent lifecycle and several one-off utilities that don't connect to the rest.

Resources