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Glama

Property Records

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,767 across 1506 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses the extraction-only behavior, the exact success return shape, the explicit refusal shape with the full refusal_reason enum, and the extra LLM call cost. No contradiction with 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 front-loaded with the primary purpose and every sentence adds value, but it is a dense single paragraph containing large literal return/refusal shapes and source statistics. It is appropriately sized but slightly less scannable than it could be.

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 carries the burden of explaining return values, and it does so thoroughly with both success and refusal shapes. It also covers selection criteria, cost, and grounding guarantees, leaving no critical gap for an agent to invoke it correctly.

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 description coverage is 100% and the schema already documents the question parameter and its aliases. The description adds no parameter-specific semantics beyond the high-stakes context, so it earns the baseline rather than higher.

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 names a specific, differentiated capability: a hallucination-resistant answer mode that routes like ask_pipeworx but extracts the answer only from the tool result. It explicitly contrasts with ask_pipeworx, so an agent can tell them apart.

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?

It states exactly when to use the grounded mode ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), including the cost trade-off. This is exemplary routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Property Records toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants of the same router, and there are six polymarket-related tools with overlapping arb/edge/fill-risk purposes. Property-specific tools are distinct but buried among many unrelated meta-tools.

Naming Consistency3/5

All names use snake_case, but the structural pattern is inconsistent: some are verb_noun (ask_pipeworx, validate_claim), others noun_verb (property_lookup), and many are noun_noun (entity_profile, polymarket_arbitrage). No clear systematic convention across the set.

Tool Count2/5

33 tools is far too many for a server labeled 'Property Records'—only two tools (property_lookup, property_coverage) actually serve that purpose. The rest belong to unrelated domains (general data lookup, prediction markets, memory, subscriptions), making the surface feel bloated and unfocused.

Completeness2/5

For a property-records server, the surface is incomplete: it only provides lookup plus a coverage matrix, with no other property-related operations (e.g., tax history, comparable sales) and no way to handle unsupported jurisdictions beyond a simple flag. The unrelated tools do not contribute to the stated domain, leaving the core purpose thinly covered.

Resources