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Glama

Leetcode

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

A4.3/5.0
Behavior5/5

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

Despite strong annotations (readOnlyHint, etc.), the description adds crucial behavior: the two-path routing (SEC EDGAR vs grounded), the verdict values, and, importantly, the distinction between could_not_verify (check didn't happen) and unsupported (no source), with a specific warning not to show could_not_verify as evidence. This is valuable behavioral disclosure 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.

Conciseness4/5

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

The description is lengthy but every sentence carries important information. It leads with trigger phrases and examples, then proceeds logically through routing, output, and caller warnings. Slightly dense but not bloated; a small amount of trimming could improve structure, but it's 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?

Given the absence of an output schema, the description compensates by listing possible verdicts, mentioning citation and reasoning, and covering error cases. It also explains the tool's value proposition (replaces 4-6 sequential calls). This makes the description complete for an agent to use the tool effectively.

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 covers 100% of parameters, so baseline is 3. The description adds useful semantics for tolerance_pct: it overrides the implied tolerance, defaults to a cap of 5, and suggests 1-2 for hallucination detection. This goes beyond the schema's description, so a 4 is warranted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function: verifying natural-language factual claims against authoritative sources, with trigger phrases and a specific verb ('verify'). It distinguishes the tool's scope by focusing on claim verification, but it does not explicitly name sibling tools or state how it differs from similar tools like ask_pipeworx_grounded, so it falls just short of a 5.

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?

Provides explicit 'Use whenever the agent needs to check whether something a user said is factually correct.' It also describes the routing between financial and other claims, giving clear context. However, it does not mention alternative tools or when not to use this tool, so it lacks the exclusions needed for 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

B3.1/5.0
Disambiguation2/5

Multiple tools have overlapping roles: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve the same lookup purpose, and there are six different Polymarket tools with similar names and functions. The inclusion of a large unrelated data platform alongside a few LeetCode tools makes selection additionally confusing.

Naming Consistency4/5

Almost all tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, list_subscriptions, etc.). Minor exceptions like 'problem' and 'daily_question' are still readable and don't break the overall predictability.

Tool Count2/5

37 tools is far too many for a server named 'Leetcode' — the vast majority are unrelated Pipeworx data, prediction-market, and memory tools. Even as a general data server the count is heavy, and for the apparent LeetCode purpose it is severely over-scoped.

Completeness2/5

The LeetCode-specific tools cover basic user stats and problem details but lack problem listing/search, submissions, or any interaction beyond read-only queries. The Pipeworx side is extensive but irrelevant to the server's stated purpose, so the core domain has significant gaps.

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