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Glama

Crypto Feeds

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

A4.5/5.0
Behavior5/5

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

Annotations already provide read-only/idempotent safety context, but the description goes further by disclosing failure semantics: 'could_not_verify' means the check did not happen and must not be treated as evidence, while 'unsupported' means no source exists. It also reveals internal routing and replacement of multiple sequential calls, adding substantial behavioral transparency.

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?

Although long, the description is front-loaded with trigger phrases, followed by usage, pipeline, return values, critical caller warnings, and efficiency rationale. Every sentence contributes essential information for correct invocation and interpretation, and the structure makes it easy to scan.

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 listing all verdict values, explaining 'could_not_verify' and 'unsupported' distinct failure modes, mentioning the actual value plus citation, and covering both financial and non-financial claim processing. It is comprehensive enough for an agent to confidently invoke and interpret results.

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% with detailed descriptions for both parameters, so the schema carries the primary meaning. The description adds slight reinforcement about tolerance overriding claim wording and the hallucination-detection use case, but it does not significantly expand beyond what the schema already states—hence the baseline 3.

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 explicitly identifies the tool's purpose as natural-language claim verification, with trigger phrases like 'fact check' and 'verify the claim that…'. It clearly distinguishes this from sibling research/Q&A tools by emphasizing verdict output and the specific two-path processing (SEC EDGAR for financial claims, grounded pipeline for all others).

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 states 'Use whenever the agent needs to check whether something a user said is factually correct,' which gives a clear context. It also explains the scope of financial versus non-financial claims, but it does not explicitly name sibling alternatives or exclusion criteria, leaving some room for ambiguity with tools like ask_pipeworx_grounded.

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

A3.8/5.0
Disambiguation2/5

Several tools are nearly indistinguishable in role: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are all variants of the same router, with the beta version explicitly noted as currently identical to the stable one. The six Polymarket tools also overlap heavily around edge-finding, arbitrage, and fill-risk analysis, making misselection likely.

Naming Consistency3/5

Names are uniformly snake_case and groupable into prefixes like polymarket_* and pipeworx_*, but the set does not follow a consistent verb_noun convention. Noun-first names like entity_profile and recent_alerts sit alongside verb-first names like read_feed and validate_claim, and product-name suffixes such as ask_pipeworx_beta/grounded add further inconsistency.

Tool Count2/5

At 34 tools, the surface is well past the 25+ threshold and far broader than the 'Crypto Feeds' name suggests. The set spans feed reading, general data research, prediction markets, AI-brand visibility audits, npm dependency scanning, memory, and subscriptions, making it feel like a platform-wide dump rather than a focused MCP server.

Completeness3/5

The broad research workflow is well covered with query, grounded answer, deep research, entity profiles, comparisons, fact-checking, and entity resolution. However, feed functionality is read-only with no feed management, subscription types do not include crypto feeds despite the server name, and there is no dedicated tool for resolving the advertised pipeworx:// citation URIs.

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