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Glama

Mbta

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds significant behavioral context: the routing fast path for SEC EDGAR vs. grounded pipeline, the verdict set, the 'verbatim evidence' requirement, and critically the caveat about could_not_verify being a non-evidence error, plus unsupported meaning no source coverage. This goes well beyond annotations and 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.

Conciseness5/5

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

The description is detailed but every sentence contributes: examples, usage, routing, return type, critical error semantics, and efficiency gains. It is front-loaded with the most important purpose, and the warnings are clearly highlighted with 'IMPORTANT for callers.' No filler or redundancy.

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 by enumerating the possible verdicts, stating that it returns 'the grounded or structured actual value with pipeworx:// citation, and reasoning.' It also explains the failure semantics for could_not_verify and unsupported. This is sufficient for an agent to use the tool correctly without needing further documentation.

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?

Schema coverage is 100% for both parameters, and the schema already explains the tolerance_pct semantics including the 1–2 override for hallucination detection and default cap. The tool description adds no parameter-specific meaning beyond what the schema provides, so the baseline 3 applies.

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 opens with concrete natural-language query examples and clearly states the tool's function: 'natural-language claim verification against authoritative sources.' It specifies the resource (claims/facts) and the action (verify/check), and distinguishes its scope with the financial-versus-other routing detail. This makes it highly specific and clearly differentiates it from general Q&A tools.

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 states when to use this tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing for company-financial vs. other claims. However, it does not name alternative sibling tools or provide explicit 'when-not' guidance, so it falls just 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

A3.9/5.0
Disambiguation2/5

ask_pipeworx_beta is explicitly stated to be currently identical to ask_pipeworx, which is a direct duplication. The polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes around finding and validating betting edges, and the ask_pipeworx / ask_pipeworx_grounded / deep_research / validate_claim tools all handle natural-language 'look up X' queries, making misselection likely without reading lengthy descriptions.

Naming Consistency4/5

snake_case is uniform and helpful prefixes (mbta_, polymarket_, pipeworx_, ask_pipeworx) create recognizable families. However, verb style is inconsistent — imperative verbs like ask/compare/discover/validate mix with noun-first names like bet_research, entity_profile, and search_within, and the memory trio (remember/recall/forget) doesn't share a common prefix.

Tool Count2/5

At 35 tools this exceeds the comfortable range, and the count is inflated by near-duplicates (ask_pipeworx_beta) and a dense 6-tool polymarket family. The server also mixes unrelated domains — only 4 of 35 tools are MBTA transit tools while the rest are Pipeworx data research, prediction markets, memory, and subscriptions — making it a kitchen sink rather than a well-scoped set.

Completeness4/5

The Pipeworx research surface is thorough: query, grounded verification, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscriptions are all covered with few dead ends. Minor gaps exist — the MBTA portion lacks schedule/line-detail tools beyond departures and alerts, and the AI-visibility feature feels bolted on without deeper integration.

Resources