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Arcgis Montana

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

A4.9/5.0
Behavior5/5

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

The description adds rich behavioral details beyond the annotations: it uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, enforces a 200K character cap with truncation flagging, and returns passages with character offsets and similarity scores. These details disclose how the tool executes and what limitations exist, consistent with the readOnly and idempotent hints.

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 four sentences, each earning its place: the first establishes the core action, the second explains when to use, the third provides a companion workflow, and the fourth details technical behavior. No filler or redundant phrasing.

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 lacking an output schema, the description specifies the return format (top-N passages with character offsets and similarity scores). It also covers the input size limit, truncation behavior, and safety profile (read-only). For a tool with only three parameters, this description is fully complete.

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?

The schema already describes all three parameters at 100% coverage, so the baseline is 3. The description adds value by giving concrete examples of text inputs (SEC 10-K body, article, long tool result) and clarifying the query as 'natural-language query.' This enriches the meaning of the 'text' and 'query' 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 the tool's purpose: 'Semantic search INSIDE a fetched record.' It distinguishes itself from siblings like ask_pipeworx_grounded by emphasizing it operates on text you already have, not a global knowledge base. The verb 'search' and resource 'fetched record' are specific and unambiguous.

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?

The description explicitly advises when to use the tool: 'Use when the record is too big to cram into the prompt.' It also provides an alternative workflow, pairing with ask_pipeworx_grounded, to ground over relevant passages. This gives the agent clear decision guidance.

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
Disambiguation3/5

Several clusters of similar tools exist: ask_pipeworx, ask_pipeworx_beta (identical right now), and ask_pipeworx_grounded are easily confused, and the many polymarket_* tools overlap heavily. However, the extremely detailed descriptions usually clarify the specific intent, so an agent can often pick correctly.

Naming Consistency4/5

Tool names are uniformly snake_case and mostly follow verb_noun or a recognizable prefix pattern (ask_, polymarket_, pipeworx_). There are a few noun-phrase exceptions like layer_info and entity_profile, but the style is consistent enough that naming is not a major source of confusion.

Tool Count2/5

34 tools is heavy for any server, and especially for one named 'Arcgis Montana' where only three tools (search_datasets, query_layer, layer_info) relate to GIS. Most of the surface is a general-purpose data/analytics API, making the count feel bloated and unfocused.

Completeness2/5

Relative to the implied ArcGIS Montana purpose, the surface is severely incomplete: it only supports searching and querying datasets, with no create/update/delete or administrative capabilities. The Pipeworx functionality is broad, but the GIS side is a thin slice, leaving obvious gaps for geospatial workflows.

Resources