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Security 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

A5/5.0
Behavior5/5

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

The description goes well beyond the annotations by explaining the fast-path vs grounded-pipeline behavior, the meaning of each verdict type, and the critical caveat that could_not_verify carries a verification_error and must not be treated as evidence for or against the claim. It also clarifies that unsupported means no coverage in the source base. No contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false).

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?

The description is richly detailed but every sentence earns its place: trigger phrases, use case, routing logic, return value summary, and critical caller warnings. It front-loads the core purpose and then layers behavioral details, maintaining a clear logical structure (purpose → usage → outputs → caveats) without 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?

Given the tool's complexity and the absence of an output schema, the description fully compensates by enumerating the six possible verdicts, the returned value with citation, and reasoning. It also covers failure modes (verification_error) and the difference between could_not_verify and unsupported, making the behavior fully comprehensible to an agent deciding whether and how to invoke the tool.

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?

The input schema already has 100% coverage with detailed parameter descriptions, but the description adds significant semantic value: it explains how tolerance_pct overrides the implied tolerance, recommends specific values (1–2) for hallucination detection, and clarifies the default cap at 5. The claim parameter is enriched with examples and the context of structured vs grounded verification.

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 clearly identifies the tool as natural-language claim verification with specific verb phrases like 'fact check' and 'verify the claim that…'. It explicitly states the scope (checking factual correctness) and distinguishes the two routing paths (SEC EDGAR for company financials, grounded pipeline for everything else), setting it apart from sibling tools like deep_research or bet_research.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides crucial usage guidance about interpreting results, particularly the distinction between could_not_verify (check did not happen) and unsupported (no source found), which prevents misuse. The note about setting tolerance_pct=1–2 for hallucination detection adds concrete operational 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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio creates real selection ambiguity — beta is currently identical to the stable router, and grounded shares the same routing with an added evidence step. Polymarket tools also overlap (polymarket_edges vs polymarket_arbitrage both surface structural arbitrage), and entity_profile/recent_changes both pull filings and news.

Naming Consistency4/5

Most tools follow a snake_case verb_noun pattern (resolve_entity, list_feeds, validate_claim, compare_entities). Minor deviations exist — bare verbs (remember, recall, forget, subscribe, unsubscribe) and noun-first names (entity_profile, recent_changes, pipeworx_trending) — but the overall convention is consistent and readable.

Tool Count2/5

34 tools is excessive for a server nominally named 'Security Feeds' — only three tools actually relate to security feeds. Even as a broad data-research platform, the surface feels bloated with five near-exclusive Polymarket tools, three memory tools, and four subscription-lifecycle tools that could be consolidated.

Completeness4/5

For the actual domain revealed by the tools (data research, entity intelligence, prediction markets, subscriptions, feeds), coverage is strong: resolution, profiles, changes, comparison, claim verification, memory CRUD, subscription lifecycle, and feed operations are all present. Minor gaps include no subscription-update tool and no direct feed-search tool, but the ask_pipeworx router compensates.

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