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Nuget

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

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description details verdict semantics, especially the critical warning that could_not_verify is not evidence and must not be shown as one. It also discloses source-routing behavior (SEC EDGAR fast path vs grounded pipeline), adding substantial behavioral context.

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 longer than average but well-structured, leading with concrete user-phrase examples, then usage guidelines, behavior, return values, and a replacement note. Every section adds value, though the pipeline-routing explanation could be slightly tightened without losing meaning.

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 explains return values (verdict set, actual value, citation, reasoning) and error semantics for could_not_verify and unsupported. For a tool with just two simple parameters, this is complete and self-sufficient.

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?

The input schema already provides 100% description coverage for both 'claim' and 'tolerance_pct'. The tool description does not add parameter-specific details beyond what the schema states, so a baseline score of 3 is appropriate.

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 with specific verb 'validate' and resource 'claim'. It provides realistic trigger phrases and explicitly contrasts itself with sibling tools by noting it replaces 4–6 sequential calls, aiding selection.

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 states when to use ('Use whenever the agent needs to check whether something a user said is factually correct') and gives nuanced routing for company-financial vs other claims. It does not name alternative tools to avoid, but the context is strong enough for an agent to decide.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same function, ask_pipeworx_grounded is the same router with a different response mode, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all overlap on prediction-market edge detection. search vs search_within vs discover_tools also blur discovery boundaries. An agent would struggle to pick the right tool without reading every long description.

Naming Consistency3/5

All names are snake_case and individually readable, so there's no chaotic style mixing. However, the pattern is inconsistent: bare verbs (search, recall, forget, subscribe), verb_noun (get_package, resolve_entity, scan_dependency), noun phrases (latest_version, recent_alerts), and compound prefixes (pipeworx_*, polymarket_*). The server is named 'Nuget' but the vast majority of tools carry pipeworx_ or polymarket_ prefixes, making the namespace feel like a grab-bag.

Tool Count2/5

35 tools is far too many for a server ostensibly named 'Nuget' — only ~5 tools relate to NuGet package lookup (search, get_package, list_versions, latest_version, scan_dependency), and even scan_dependency is npm-only. The remaining ~30 tools belong to an unrelated Pipeworx research/markets/memory platform. The count is inflated by redundant variants (ask_pipeworx trio, six polymarket tools) rather than distinct functionality.

Completeness2/5

Judged by the server's stated purpose (NuGet), the surface is thin and has dead ends: search and version metadata are covered, but there's no package owner/publisher info, no readme/description body fetch, no download stats beyond totals, and scan_dependency targets the wrong ecosystem (npm). Judged by the actual dominant domain (Pipeworx), coverage is excessive and sprawling. The tool set fails to deliver a coherent, complete surface for either apparent purpose.

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