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Glama

Chile Procurement

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?

Even though annotations already declare the tool read-only, idempotent, and non-destructive, the description adds critical behavioral context: it reveals two distinct processing paths (SEC EDGAR for financials vs grounded pipeline for everything else), defines the full verdict taxonomy, and warns that 'could_not_verify' means the check did not happen and must not be treated as evidence. This goes well beyond what annotations provide.

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 structured with trigger phrases first, then usage guidance, pipeline routing, return values, and error semantics. Every sentence serves a purpose, no fluff. While it is long, the length is justified by the tool's complexity and the need to convey error-handling nuances. Front-loaded with the most important info.

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?

Given the tool's complexity, no output schema, and rich annotations, the description is complete. It explains the return verdicts, the actual value with citation, reasoning, and the distinct meaning of 'could_not_verify' vs 'unsupported'. It also clarifies the input claim and tolerance_pct semantics. The description leaves no significant gaps for an agent to misuse the 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?

The schema already covers both parameters with descriptions (100% coverage). The description adds extra value by explaining that tolerance_pct overrides claim wording defaults, suggesting 1–2 for hallucination detection, and noting the default is capped at 5. Examples for the claim parameter also clarify expected natural-language format. This exceeds the baseline 3 but is not a full 5 since the schema already does the heavy lifting.

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 states a specific verb+resource: natural-language claim verification against authoritative sources. It includes trigger phrases ('fact check', 'verify the claim that...') and explicitly distinguishes this from general Q&A tools by returning a verdict. The description also clarifies it replaces 4–6 sequential calls, making its purpose distinct from siblings.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides examples. It also explains that company-financial claims go through one path and other claims through a grounded pipeline, which helps callers understand the internal routing. However, it does not explicitly list when NOT to use the tool or name alternative sibling tools, so it stops short 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

A3.5/5.0
Disambiguation2/5

Many tools are distinct, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) heavily overlaps—the beta is explicitly identical to the stable version. The two Chile-specific tools are clear, but the presence of numerous unrelated data tools creates confusion about which tool serves the server's purported purpose.

Naming Consistency2/5

Naming is inconsistent: some tools follow verb_noun snake_case (chile_get_tender, chile_search_tenders), others use plain verbs (ask_pipeworx) or noun_verb patterns (polymarket_arbitrage, entity_profile). Mixed conventions and varying levels of specificity make the set feel uncoordinated.

Tool Count2/5

33 tools is excessive for a server named 'Chile Procurement'—only two tools relate to Chile procurement, while the rest are generic Pipeworx data utilities. The count is not scoped to the server's stated purpose; it appears to be a bundled general-purpose toolkit rather than a focused procurement interface.

Completeness2/5

For Chile procurement, only search and get-detail are provided; there is no ability to list all historical tenders, filter by category or amount, or track bidding. The read-only surface covers basic retrieval but lacks common procurement workflows. The broader data tools are complete individually but irrelevant to the server's domain.

Resources