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Glama

Arcgis Dukescounty

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

A4.8/5.0
Behavior5/5

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

The description discloses key behaviors beyond annotations: returns top-N passages with character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char windows, and truncates inputs over 200K chars with a flag. Annotations (readOnly, openWorld, idempotent, non-destructive) are consistent and no contradiction exists.

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 information-dense but well-structured, front-loading the core purpose, then usage context, then technical details. It is a bit long, but every sentence adds value without 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?

Given the absence of an output schema, the description adequately covers return values (passages, offsets, similarity scores), usage context, technical implementation, limits, and how it pairs with other tools. It is fully self-contained for an agent to invoke correctly.

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% with each parameter described clearly. The description adds extra meaning by explaining that 'limit' corresponds to top-N passages, 'text' is the document to search, and 'query' takes natural-language examples. It also mentions character offsets and similarity scores, which enriches parameter semantics beyond the schema.

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 function: semantic search inside a fetched record. It gives specific examples (SEC 10-K, article) and differentiates from siblings by emphasizing 'INSIDE' a record and by referencing ask_pipeworx_grounded as a companion, making the purpose unmistakable.

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?

Explicit guidance is provided: use when a record is too large to fit in the prompt, and pair with ask_pipeworx_grounded to ground over relevant passages. This clearly states when to use the tool and mentions an alternative workflow.

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

All Arcgis Dukescounty toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes that require reading long descriptions to separate. The server name promises ArcGIS Dukes County but most tools are unrelated, making the overall selection space confusing.

Naming Consistency3/5

Most names are snake_case, but conventions vary: some are verb_noun (query_layer, search_datasets, list_subscriptions), some noun-ish (entity_profile, layer_info, pipeworx_trending), some bare verbs (remember, recall, forget, subscribe), and some long compounds (polymarket_edge_tracker, scan_competitor_ai_presence). Readable overall, but no strong consistent pattern.

Tool Count2/5

34 tools is heavy, and only three (search_datasets, layer_info, query_layer) relate to the server's apparent ArcGIS Dukes County purpose. The rest form a sprawling Pipeworx/prediction-market/memory/utility toolkit, creating a severe scope mismatch with the server's name.

Completeness2/5

For the named Dukes County GIS domain, the surface is minimal—dataset discovery, schema, and queries—with no spatial operations or editing, though read-only access may be acceptable. For the broader Pipeworx functionality it is fairly comprehensive, but that is not what the server name advertises, leaving the set incomplete relative to its apparent identity.

Resources