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Glama

Cricket

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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (read-only, idempotent, open-world), the description discloses the two internal pipelines, the full set of verdict values, the meaning of 'could_not_verify' (with verification_error structure) vs. 'unsupported', and how citations are returned. This adds substantial behavioral context not present in the annotations. No contradiction exists.

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 each sentence earns its place: trigger phrases, core directive, pipeline specifics, return contract, and error semantics. It is front-loaded with the most actionable information (when to use), then layers detail, making it dense yet well-organized 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?

With no output schema, the description fully bears the return-value burden and delivers: it names all six verdicts, explains the critical distinction between 'could_not_verify' and 'unsupported', and specifies the error payload. This is sufficient for an agent to correctly invoke the tool 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% and both parameters are described in detail, so the baseline is 3. The tool description itself does not add parameter semantics; it focuses on usage and return behavior. The schema's examples and tolerance_pct explanation already handle the load, so no extra credit is warranted.

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 explicitly states the tool verifies factual claims against authoritative sources. It distinguishes itself from siblings by naming the resource ('claim verification'), the dual routing (financial vs. grounded), and the output verdict types, making its 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use whenever the agent needs to check whether something a user said is factually correct' provides clear invocation context. It further differentiates when the SEC EDGAR/XBRL fast path applies versus the grounded pipeline, and notes it replaces 4–6 sequential calls, giving an explicit usage directive with no ambiguity.

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

The set mixes several near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, current_matches and match_scores largely duplicate each other, and the Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) has fuzzy boundaries. Long descriptions help, but an agent would often need to read deeply to pick the right tool.

Naming Consistency3/5

Most names are lowercase snake_case, but conventions vary: some are verb_noun (ask_pipeworx, scan_dependency, compare_entities), some are noun phrases (current_matches, match_info, entity_profile), and there are mixed prefixes (pipeworx_*, polymarket_*, plain names). It is readable but not a coherent naming system.

Tool Count2/5

35 tools is heavy, and only four of them (current_matches, match_info, match_scores, search_players) relate to the server's apparent cricket purpose. The rest are a sprawling general-purpose data/research/prediction-market/utility toolkit, making the surface feel bloated and off-scope.

Completeness2/5

As a cricket server, the surface is shallow: it has live matches, scores, match info, and player search, but no player stats, batting/bowling figures, team profiles, series/schedules, or historical match data. The many unrelated tools do not fill these obvious cricket-domain gaps.

Resources