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Congress

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. Added

TDQS

A5/5.0
Behavior5/5

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

Annotations only declare safety hints (read-only, idempotent). The description adds substantive behavior: the two verification paths, exact percent-delta math, verdict values, return of actual value with citation and reasoning, and the distinction that could_not_verify is a failed check not evidence either way. This goes well beyond the 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?

The description is dense but not bloated. It front-loads example phrases and clear use cases, then systematically covers behavior, return values, error semantics, and efficiency. Every sentence carries information relevant to the agent's decision-making, with no filler.

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 compensates by enumerating the possible verdicts, the actual value + citation + reasoning return, and the error/semantic distinctions. It also explains unsupported and could_not_verify in a way that covers edge cases, making the tool's behavior predictable without an output schema.

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?

Though schema coverage is 100%, the description enriches both parameters. It explains that tolerance_pct overrides the claim-wording-implied tolerance, gives a practical range (0.5–50), suggests 1–2 for hallucination detection, and clarifies the default ('implied by wording, capped at 5'). This goes beyond the schema's field descriptions.

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 opens with natural-language trigger phrases and a specific verb+resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes the tool's scope (fact-checking user statements) and even notes it replaces 4–6 sequential calls, setting it apart from lower-level sibling tools.

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 conditional routing (SEC EDGAR for company-financial claims, grounded pipeline for all else) and gives critical guidance on how to interpret could_not_verify vs unsupported, preventing misuse.

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

Multiple tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_ipeworx_grounded, and deep_research all answer questions; disover_tools, suggest_questions, and pipeworx_trending all serve discovery; and five polymarket_* tools plus bet_research overlap heavily on prediction-market opportunity detection. The descriptions are detailed, but the boundaries are subtle enough that an agent can easily pick the wrong tool.

Naming Consistency4/5

The set is uniformly lowercase snake_case and uses recognizable prefixes such as ask_pipeworx, polymarket_, pipeworx_, and get_, which makes the naming fairly predictable. It is not a strict verb_noun convention — some names are noun phrases like entity_profile or ai_visility_check — but the overall style is consistent.

Tool Count2/5

With 36 tools, the surface is far heavier than the 'Congress' name suggests: only five tools are actually about congressional data, while the rest are general research, memory, subscription, and meta utilities. Many of these overlap, so the count feels bloated rather than well-scoped.

Completeness3/5

For Congress-specific work, the core needs are covered: search bills, get bill details, list members, and retrieve recent votes. However, deeper legislative operations like member voting records, committee actions, and amendments are missing, and the surrounding Pipeworx tools do nothing to close that gap. As a general data-research platform it is broad, but its actual 'Congress' identity feels incompletely realized.

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