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Glama

Simap

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. First observed

TDQS

A5/5.0
Behavior5/5

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

The description adds significant behavioral detail beyond annotations: it explains the meaning of each verdict type (e.g., could_not_verify means the check did not happen and carries an error, unsupported means no source found). It also clarifies that could_not_verify is not evidence for or against the claim, which is crucial for correct caller behavior. No contradiction with readOnly/openWorld/idempotent hints.

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 thorough but every sentence provides value: trigger phrases, use cases, routing logic, return value breakdown, and error semantics. It is front-loaded with examples and action-oriented language. While dense, no information is redundant or wasteful.

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 compensates fully by detailing the return values (verdict, actual value, citation, reasoning) and the distinctive semantics of could_not_verify vs unsupported. It also covers the internal routing logic and the benefit over multi-step calls, providing a complete picture for an agent to invoke the tool correctly and interpret results.

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?

Despite 100% schema coverage, the description enriches parameter semantics. For tolerance_pct, it explains that it overrides the tolerance implied by claim wording and suggests 1-2% for hallucination detection, adding practical guidance beyond the schema's generic description. The claim parameter includes illustrative examples.

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 a natural-language claim verification tool with specific verbs ('validate', 'verify', 'fact check'). It distinguishes itself from general Q&A siblings by focusing on assessing truthfulness and providing verdicts, 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?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR fast path) and other claims (grounded pipeline), and notes that it replaces 4-6 sequential calls, providing strong usage context.

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

There are several tools with overlapping query responsibilities: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-variants of the same router, and bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunity discovery. An agent must read the long descriptions carefully to distinguish them, and some variants are behaviorally identical today.

Naming Consistency3/5

All names are readable snake_case, but the verb_noun convention is not consistently applied: some are clean verb phrases like validate_claim and discover_tools, while others are noun phrases like entity_profile, recent_alerts, or simap_project. The prefixed groups (polymarket_*, pipeworx_*) help, but the overall naming style is mixed.

Tool Count2/5

34 tools is above the heavy range, and the set is inflated by redundant router variants, five overlapping Polymarket tools, and loosely related utilities like generate_llms_txt and scan_dependency. The server is named Simap but only three tools relate to Swiss procurement, making the scope feel bloated and misaligned.

Completeness4/5

For an information-retrieval-style server, the core workflows are well covered: discovery, routing, grounded answers, entity resolution, profiles, comparisons, fact-checking, subscriptions, and memory. The main gaps are minor, such as updating a subscription in place or deeper native Simap-specific actions, and those can be worked around.

Resources