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Glama

Realestate

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: truncation at 200K chars with flag, embedding model (BGE-base-en), window size (500-char overlapping), cosine similarity, and offsets for verification. Annotations cover safety (readOnly, idempotent); description enriches technical behavior.

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?

Compact yet comprehensive: first sentence states purpose, second gives usage scene, third provides technical internals. No redundant phrases; every sentence earns its place.

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?

Despite no output schema, the description explains return format (passages with offsets and scores), cap and truncation behavior, and technical basis. All user-facing behavior is covered.

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 coverage is 100%, so baseline is 3. Description adds value: example queries for 'query', default value for 'limit', and reiterates the truncation cap for 'text'. This goes beyond the schema descriptions.

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 uses specific verb+resource ('Semantic search INSIDE a fetched record'), gives concrete examples (SEC 10-K, article), and distinguishes itself by mentioning a sibling tool (ask_pipeworx_grounded) and the offset feature.

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?

Explicitly states when to use: 'Use when the record is too big to cram into the prompt', explains context savings, and pairs with ask_pipeworx_grounded. Lacks explicit when-not-to-use but provides clear context.

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

A3.8/5.0
Disambiguation2/5

Multiple tools occupy the same natural-language lookup niche: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions, and the beta tool is currently described as identical to the stable router. Prediction-market edge detection also fans out across bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, and polymarket_fill_risk, so an agent can easily select the wrong one.

Naming Consistency4/5

Most names follow a predictable snake_case action-first pattern (ask_pipeworx, resolve_entity, subscribe, unsubscribe) with helpful domain prefixes for polymarket_*, realestate_*, and pipeworx_*. Minor deviations exist—entity_profile is noun-first, ask_pipeworx lacks an underscore, and remember/forget/recall are bare verbs—but they do not create real confusion.

Tool Count2/5

33 tools is well above the coherence sweet spot and the rubric's 25+ threshold. The count is inflated by auxiliary platform utilities (feedback, trending, memory, subscriptions, llms.txt generation, npm scanning) that are unrelated to the Realestate name and make the tool surface feel like a full platform rather than a focused MCP server.

Completeness2/5

For a server named Realestate, the surface is only minimally complete: realestate_municipalities and realestate_transactions cover Japanese transaction lookups, but there are no tools for property listings, property details, pricing estimates, or typical real-estate workflows. Even viewed as a broad data platform, the set is read-heavy with no create/update/delete operations beyond memories and subscriptions, leaving significant workflow gaps.

Resources