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Glama

Arcgis Peterborough

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?

Beyond the readOnlyHint and idempotentHint annotations, the description discloses the embedding model (BGE-base-en), cosine similarity over 500-char overlapping windows, the 200K character cap with truncation flagged, and that returned passages carry character offsets for verification. This gives deep insight into the tool's behavior without contradicting 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 concise yet information-dense, front-loading the core purpose, then usage context, technical details, and limitations. Every sentence adds value; there is no filler or repeated schema info.

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?

The tool has no output schema, so the description's mention of return values (passages, character offsets, similarity scores) is essential and well-covered. It also addresses edge cases (200K cap, truncation) and integration with sibling tools, making the description complete for an agent to select and invoke the tool 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%, but the description enriches the parameter meaning: it specifies what 'text' should be (fetched record like SEC 10-K), provides query examples ('supply-chain risk', 'drug interactions with warfarin'), and implies 'limit' via 'top-N'. While not necessary, this adds practical context that helps the agent formulate correct inputs.

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' with the ability to pass text and a natural-language query and receive top-N passages with offsets and scores. It distinguishes itself from siblings like ask_pipeworx_grounded by positioning itself as the retrieval step after fetching a record, making the purpose 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?

Explicit usage guidance is provided: 'Use when the record is too big to cram into the prompt' and it explicitly pairs with ask_pipeworx_grounded: 'fetch with the gateway, ground over the relevant passages instead of the whole document.' This tells the agent when to use this tool and how it relates to alternatives.

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

A3.6/5.0
Disambiguation2/5

Multiple tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying 5,767 tools with significant functional overlap. Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) also have blurred boundaries around edge detection and fill risk. The ArcGIS tools (search_datasets, layer_info, query_layer) are distinct, but the non-ArcGIS tools dominate and create confusion.

Naming Consistency2/5

The naming conventions are inconsistent across the set. Some tools use verb_noun (ask_pipeworx, query_layer, search_datasets, list_subscriptions), some use bare verbs (forget, recall, subscribe, unsubscribe), and others use descriptive multi-word names (polymarket_fill_risk, scan_competitor_ai_presence, generate_llms_txt). The ask_pipeworx family and polymarket_* family are internally consistent, but the overall set mixes styles without a clear pattern.

Tool Count2/5

34 tools is heavy for a server that appears to be an ArcGIS data server but includes a massive Pipeworx data-research and prediction-market subsystem. The ArcGIS portion only has 3 tools (search_datasets, layer_info, query_layer), while the rest form a separate general-purpose research/betting toolkit. The count feels bloated and unfocused relative to the server's stated name.

Completeness2/5

The ArcGIS surface is incomplete: search_datasets, layer_info, and query_layer offer no update/create/delete or metadata exploration beyond one layer at a time. The Pipeworx portion is broad but lacks clear lifecycle coverage for subscriptions (create/cancel works, but no update), and the memory tools (remember/recall/forget) are peripheral. The set feels like an accidental aggregation of unrelated domains rather than a complete surface for one purpose.

Resources