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Glama

Arcgis Kansas

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?

Annotations already mark it read-only, idempotent, non-destructive. The description goes further by disclosing the embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, the 200K char cap, and that longer inputs are truncated and flagged. These are valuable behavioral details 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.

Conciseness5/5

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

The description is compact and front-loaded. It opens with the core purpose, then moves to usage context, then to technical specifics. Every sentence adds value—no fluff or repetition.

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 the return format (passages with offsets and similarity scores), the input constraints (200K cap, truncation), and the use case. Combined with full parameter descriptions and strong annotations, it provides a complete picture 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 coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides (e.g., the schema already explains 'text', 'query', and 'limit'). It mentions general concepts like 'character offsets' but does not enhance the meaning of individual parameters.

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 states it is 'Semantic search INSIDE a fetched record' with a specific verb and resource. It explains the inputs (text + query) and outputs (top-N passages with character offsets and similarity scores), and distinguishes itself from siblings by emphasizing it operates on an already-fetched record, not external data.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and provides a complementary alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This gives clear when-to-use and how it relates to a sibling tool.

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

A3.5/5.0
Disambiguation1/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all route questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker all deal with prediction market edges). The inclusion of memory tools (remember, recall, forget) alongside research tools further blurs boundaries.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), some use underscores with prefixes (ask_pipeworx, polymarket_arbitrage), and others are more generic (query_layer, layer_info). There is no consistent pattern across the set.

Tool Count2/5

With 34 tools, the set is overly large for a geospatial server; most tools are unrelated to ArcGIS Kansas (e.g., prediction market tools, general research tools). Only 3 tools (search_datasets, layer_info, query_layer) are pertinent, making the count excessive and unfocused.

Completeness1/5

The server claims to be an ArcGIS Kansas tool but provides only basic layer querying and dataset search. Missing essential GIS operations such as editing, spatial analysis, or advanced queries, making it severely incomplete for its stated purpose.

Resources