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Glama

Fmi

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.

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description adds crucial behavioral nuance: the difference between could_not_verify and unsupported, the presence of verification_error, and the fast-path vs grounded pipeline. No contradiction with 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?

Despite being long, the description is front-loaded with trigger phrases and each sentence earns its place: usage, routing logic, output details, and critical caveats. No filler or redundancy.

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?

Since there is no output schema, the description must explain return semantics, and it does thoroughly: all verdict values, evidence format, and the could_not_verify vs unsupported distinction. It covers error handling and pipeline routing, making it complete 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 coverage is 100%, and both claim and tolerance_pct are well-described in the schema. The description adds some context (e.g., when to override tolerance), but mostly reinforces what the schema already states, so 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 uses a specific verb ('verify') and resource ('natural-language factual claims against authoritative sources'), and opens with explicit trigger phrases. It also distinguishes itself from siblings by emphasizing verdict-based output rather than just grounded answers.

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?

It explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also describes the financial-vs-other routing and notes it replaces sequential calls, but does not explicitly name sibling tools to avoid.

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
Disambiguation4/5

Most tools are well-differentiated, with detailed descriptions clarifying their distinct purposes. However, there is some overlap between the multiple 'ask' tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, suggest_questions) and between weather tools (forecast, latest_observations, recent_observations, warnings), which could cause confusion. Overall, ambiguity is low.

Naming Consistency5/5

All 34 tool names follow a consistent lowercase_with_underscores (snake_case) pattern. Names are descriptive and predictable, such as 'ask_pipeworx', 'entity_profile', 'polymarket_arbitrage', etc. No mixing of conventions like camelCase or inconsistent verb styles.

Tool Count3/5

The server has 34 tools, which is on the higher side given its broad scope covering weather, company research, prediction markets, and general data queries. While not excessive, it could be split into more focused servers for clarity. The count feels a bit heavy but still manageable.

Completeness4/5

The tool set covers a wide range of data domains (weather, company financials, prediction markets, news, memory, subscriptions) with reasonable completeness. Minor gaps exist, such as limited weather coverage (Finland only) and no direct support for non-company entities or unofficial data sources, but core workflows are well-supported.

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