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Glama

Repology

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 readOnly/openWorld/idempotent/destructive annotations, the description discloses critical failure semantics: could_not_verify means the check did not happen and must not be treated as evidence, while unsupported means no source covers it. It also reveals the two execution paths and the citation behavior, adding significant context 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 longer than average but every sentence earns its place: trigger phrases are front-loaded, routing logic is explained, verdicts are enumerated, and the failure caveat is prominently flagged. The claim that it replaces 4–6 sequential calls is a useful efficiency signal, not fluff.

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 provides a full picture: verdict vocabulary, the error field for non-execution, the unsupported meaning, routing behavior, and a concrete efficiency justification. An agent has enough information to select and invoke the tool correctly across both financial and non-financial claims.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters at 100%, but the description adds practical semantics: tolerance_pct overrides the claim's implied tolerance, recommends 1–2% for hallucination detection, and notes the default is capped at 5%. The claim examples also clarify expected input format, going beyond 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 opens with natural-language trigger phrases and explicitly states the tool performs 'natural-language claim verification against authoritative sources.' It clearly distinguishes from research/compare siblings by focusing on fact-checking with verdicts, not open-ended research.

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 gives explicit when-to-use guidance ('Use whenever the agent needs to check whether something a user said is factually correct') and differentiates the financial-claim structured path from other claims falling through to the grounded pipeline. It does not name specific sibling alternatives or exclusion cases, but the usage context is clear.

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

B3.3/5.0
Disambiguation3/5

There is notable overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread). While descriptions differentiate them, agents may struggle to choose the appropriate one without careful reading.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities), but the Repology-specific tools break this pattern with simple nouns like maintainer, problems, project, and repositories. This inconsistency makes the overall naming feel mixed.

Tool Count2/5

With 36 tools, the set is too large for a server focused on Repology package queries. Many tools are from the Pipeworx platform and include redundant variants (e.g., ask_pipeworx_beta, polymarket_edge_tracker), inflating the count without adding substantial new functionality.

Completeness2/5

For a server named Repology, the tool surface is severely incomplete: it lacks fundamental Repology operations like detailed package comparisons, version history exploration, and repository-specific queries. Even as a general data platform, there are gaps such as no batch export or aggregate statistics.

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