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Glama

Textcase

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?

Beyond the readOnly/idempotent annotations, the description discloses the verdict vocabulary, the meaning and non-evidentiary status of could_not_verify, the unsupported case, and the potential for LLM/source failure with verification_error. This rich behavioral context helps the agent interpret results correctly.

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 long but every sentence adds value: trigger phrases, usage, routing, return types, failure semantics, and a note on call replacement. It is structured logically and front-loaded with the most important 'what/when' information.

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?

For a tool with no output schema, the description thoroughly covers the return payload (verdict, value with citation, reasoning), failure modes (could_not_verify vs unsupported), and internal routing. The agent is well-equipped to invoke it and interpret results without additional documentation.

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?

The input schema already provides complete, detailed descriptions for claim and tolerance_pct (100% coverage). The tool description adds no further parameter-level semantics (e.g., it never mentions tolerance_pct), so it relies on the schema, warranting the baseline score of 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 opens with concrete trigger phrases and defines the tool as 'natural-language claim verification against authoritative sources,' which clearly distinguishes it from other tools. It also outlines the two internal paths (SEC EDGAR for company-financial claims, grounded pipeline for others), making the purpose unambiguous.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing logic. However, it does not name sibling alternatives or provide explicit 'when not to use' conditions, so it lacks the exclusionary guidance 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

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis; and all_cases plus convert_case both handle text-case conversion. An agent will struggle to pick the right tool without deep reading.

Naming Consistency3/5

Nearly all names are lowercase with underscores, but the morphological pattern is mixed: some are verb_noun (convert_case, compare_entities, resolve_entity, scan_dependency), many are bare nouns (entity_profile, pipeworx_trending, polymarket_edges, all_cases), and a few are single-word verbs (forget, recall, remember). Still readable, but not a predictable verb_noun convention throughout.

Tool Count2/5

33 tools is far too many for a server named 'Textcase' — only all_cases and convert_case relate to the apparent purpose. The remaining 31 constitute a sprawling assortment of data research, prediction markets, memory, subscriptions, and feedback tools that have nothing to do with text casing, making the count feel bloated and misaligned.

Completeness2/5

For the stated text-case domain, the two converters cover the basic transformations but lack supporting operations like case detection, batch processing, or custom case definitions. More fundamentally, the tool surface is incoherent: the majority of tools serve foreign domains (Pipeworx data, Polymarket, subscriptions), so there is no clear domain to evaluate for completeness, and obvious gaps exist within whatever the server is meant to be.

Resources