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Glama

Performance Review

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

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description gives crucial behavioral context: the distinction between 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source covers it), the routing to live vs structured sources, and the exact percent-delta math. This materially 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense with useful information: examples, routing, verdict list, error semantics, and efficiency note. It is a bit long, but every sentence serves a purpose. The 'IMPORTANT for callers' callout is well-structured for the key caveat. Slightly verbose but appropriate for the tool's complexity.

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?

Even without an output schema, the description covers the return value (verdict, actual value, citation), enumerates all verdict types, explains the critical error semantics, and clarifies the two fallback paths. It leaves no major ambiguity about what the agent should expect when invoking this tool.

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?

Schema coverage is 100% with descriptions for both claim and tolerance_pct. The description adds value by providing example claims and explaining that tolerance_pct can be set to 1-2 for hallucination detection, which is not in the schema. It doesn't fully compensate for the range details, but adds enough to go above baseline.

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 clearly identifies the tool as natural-language claim verification ('fact check', 'verify the claim'), with explicit usage examples and a distinct output (verdict + evidence). It differentiates from sibling tools by mentioning the SEC EDGAR + XBRL fast path and the grounded pipeline, and by stating it replaces 4–6 sequential calls.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides conditional routing guidance (financial vs other claims). However, it does not explicitly name alternative sibling tools (e.g., ask_pipeworx_grounded) or state when NOT to use it, which prevents 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

A3.9/5.0
Disambiguation3/5

Several tools occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with only subtle differences in grounding and breadth, and discover_tools overlaps with suggest_questions. The prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) each have distinct mechanics but a less careful agent could easily misfire between them.

Naming Consistency3/5

Most tools follow a verb_noun pattern (ask_pipeworx, compare_entities, generate_llms_txt, list_subscriptions), but there are notable exceptions: performance_review_generate reverses the order, entity_profile is noun_noun, and recall/remember/forget are bare verbs. The pipeworx_ and polymarket_ prefixes help group families, but the mixed styles prevent a higher score.

Tool Count2/5

At 32 tools this is a heavy surface for one MCP server, exceeding the 25+ threshold where agents struggle to discover and choose among options. The breadth is understandable given the many subdomains (data lookups, prediction markets, memory, subscriptions, feedback), but the count still feels like tool sprawl rather than a tightly scoped set.

Completeness4/5

As a data/research/prediction-market platform the surface is remarkably complete: query, grounded verification, deep research, entity resolution, comparisons, claim validation, visibility audits, subscriptions, memory, and feedback loops are all covered. Minor gaps exist—there is no direct tool for bulk exports or for editing subscriptions, and some meta-tools duplicate discovery—but agents can accomplish the core workflows without dead ends.

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