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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 declare readOnlyHint and idempotentHint, but the description adds significant behavioral detail: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, a 200K-character cap with truncation flag, and character offsets for quote verification. This fully discloses internal operations and constraints 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 a single, well-structured paragraph. It front-loads the core purpose in bold, then details inputs, outputs, use cases, and implementation specifics. Every sentence contributes value with no redundancy or wasted words.

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 tool's moderate complexity, the description fully covers: inputs (with examples), output format (passages, offsets, scores), usage context (large records, pairing with sibling), implementation details (embedding model, window size, cap), and edge behavior (truncation with flag). No gaps are apparent.

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 descriptions for all three parameters. The description adds meaning beyond the schema by noting that 'text' should be pre-fetched content (e.g., SEC filing body), providing example queries for 'query', and stating the character cap. This adds useful context, elevating from baseline 3.

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 specifies inputs (text, query), outputs (passages with offsets and scores), and use case (when record is too large). It distinguishes itself from siblings by explicitly naming ask_pipeworx_grounded as a complementary tool.

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?

The description explicitly states when to use: 'Use when the record is too big to cram into the prompt.' It also mentions pairing with ask_pipeworx_grounded, giving context. It does not provide explicit when-not-to-use or alternatives beyond the named sibling, but the guidance is clear and actionable.

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 are near-duplicates or strongly overlapping: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are only mode/version variants, and discover_tools overlaps with suggest_questions, ai_visibility_check with scan_competitor_ai_presence, and the Polymarket tools with each other. An agent would frequently need a deep read of the descriptions to know which one is truly appropriate.

Naming Consistency4/5

The naming is almost entirely snake_case and mostly follows a verb_noun or domain_noun pattern, e.g. query_layer, list_subscriptions, resolve_entity, compare_entities. Minor deviations like entity_profile, pipeworx_feedback, and polymarket_arbitrage are noun-first, but the overall pattern is still recognizable and predictable.

Tool Count2/5

34 tools for a server branded 'Arcgis Tallahassee' is far too many, especially since only a handful of them are GIS-related. The rest constitute a large general-purpose Pipeworx data platform, which at this tool count becomes unwieldy and will increase an agent's selection failure rate.

Completeness3/5

The core read-only GIS flow is covered (search_datasets → layer_info → query_layer), and the Pipeworx side is broad. However, there are notable gaps: no ArcGIS service management, no layer/feature editing, no spatial operations, and no deeper GIS functions. Because the server's stated purpose is ArcGIS-focused, the overall surface is only partially complete relative to that domain.