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Ecosystems

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds substantial behavioral detail: the SEC EDGAR/XBRL fast path vs grounded pipeline, the verdict enum, citation format, and critical caller guidance about 'could_not_verify' vs 'unsupported' to prevent misinterpretation of failure states. This goes well beyond 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?

While the description is relatively long, every sentence earns its place: trigger phrases, routing logic, return contract, caller caveat, and call-count benefit. It is front-loaded with purpose and contains no fluff or repetition of schema fields.

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?

Despite having no output schema, the description fully specifies the return contract (verdict, value, citation, reasoning) and handles edge cases like 'could_not_verify' and 'unsupported.' It covers both financial and non-financial claim types, explains fallback behavior, and provides enough detail for an agent to invoke the tool correctly.

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 schema covers both parameters at 100%, but the description adds actionable semantics beyond that: for tolerance_pct, it explains how to override the implied tolerance, suggests 1-2 for hallucination detection, and states the default is capped at 5. It also provides concrete example claims for the claim parameter, so the description meaningfully enriches the schema.

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 starts with explicit trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and clearly states the tool performs 'natural-language claim verification against authoritative sources.' It distinguishes itself from sibling research/lookup tools by focusing on fact-checking with a defined verdict set and a mention of structured financial data vs grounded fallback.

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?

The description gives explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing for financial vs other claims, but it doesn't explicitly name sibling alternatives or state when not to use the tool, so it stops short of a 5.

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
Disambiguation3/5

Several tools form tight clusters that are easy to confuse: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded are near-identical in routing, and the six polymarket_* tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread, bet_research) all operate on prediction markets with overlapping names and purposes. While each has a distinct job, an agent will need to read descriptions very carefully to pick the right one, especially when many are similar.

Naming Consistency2/5

Naming conventions are mixed throughout the set: many tools use verb_noun (ask_pipeworx, compare_entities, discover_tools, resolve_entity, validate_claim), but there are also noun_noun (polymarket_edges, entity_profile, bet_research), adjective_noun (deep_research, recent_alerts), and bare verbs (forget, recall, remember, subscribe). No consistent pattern emerges, making tool names harder to predict and remember.

Tool Count2/5

At 34 tools, this server is heavy and tries to serve many unrelated domains—data research, prediction markets, package registries, memory, subscriptions, and AI visibility. The prediction-markets cluster alone accounts for six highly specialized tools that could be consolidated. The scope feels bloated rather than focused, which will overwhelm agents exploring the toolset.

Completeness4/5

Each domain within the server has solid coverage: data lookups offer stable, beta, grounded, and deep-research variants; entity analysis has profile, compare, changes, and resolve; subscriptions support create/list/delete/alert-read; memory has set/get/list/delete. Minor gaps exist (e.g., no direct package search by keyword, no tool to update a subscription), but the major workflows are covered and there are no dead ends.

Resources