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Glama

Currencyformat

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

A4.6/5.0
Behavior5/5

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

The description discloses critical behavioral traits: it returns a verdict and rationale, uses real citations, and explains the meaning of each verdict type (especially could_not_verify and unsupported). It also clarifies that the tool internally routes between SEC EDGAR and a grounded pipeline. This adds substantial context beyond the annotations (readOnly, openWorld, idempotent), with no contradictions.

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 long but information-dense: trigger phrases, routing logic, return values, caveats, and efficiency gains are all packed in. Each sentence earns its place, though the opening is a bit verbose with example phrases. Structure is logical, moving from trigger to behavior to important warnings.

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 fully specifies the return contract: the verdict enum, the actual value with citation, and reasoning. It also explains failure modes (could_not_verify vs unsupported) and the internal two-path architecture, making the tool's full behavior clear despite its complexity.

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, so the baseline is 3. The description itself does not add any parameter-specific meaning beyond what the schema already provides; the schema's claim and tolerance_pct descriptions are thorough. The description mentions 'percent-delta math' but doesn't explain the tolerance_pct parameter directly, so it adds no extra semantic 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?

The description specifically states this tool performs natural-language claim verification against authoritative sources, with trigger phrases like 'fact check' and 'verify the claim that…'. It clearly distinguishes itself from sibling research/query tools by focusing on verifying factual claims and returning 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates routing for company-financial claims vs all others, and warns when a 'could_not_verify' result should not be treated as evidence. This 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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters have overlapping functions: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly interchangeable, deep_research overlaps with the ask_pipeworx family, and the six Polymarket tools all analyze the same domain with subtle differences. discover_tools and suggest_questions also both serve as discovery entry points, making it difficult for an agent to confidently select the correct tool.

Naming Consistency3/5

All names are snake_case, but there is no uniform structural pattern. Verb_object names like format_currency and resolve_entity coexist with noun_phrases like entity_profile and polymarket_arbitrage, bare verbs like remember and forget, and adjective_noun forms like recent_alerts. Cluster-specific prefixes are consistent, but the overall convention is mixed.

Tool Count2/5

With 33 tools, the set is substantially over-scoped and exceeds the suggested 3-15 range. Many tools could be consolidated, such as the three ask_pipeworx variants, the six Polymarket tools, and the two formatting utilities. The broad domain justifies some size, but the count feels inflated and will burden agents with excessive choice.

Completeness3/5

The server covers many areas thoroughly: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and research tools span lookup, profiling, comparison, and verification. However, there are notable gaps: pipeworx:// citation URIs are returned but no tool explicitly fetches or reads a record by URI, and there is no direct way to manage account-level settings beyond memory. These missing operations force agents to work around limitations.

Resources