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Glama

Zoho_crm

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description greatly exceeds what annotations provide. It discloses the embedding model (BGE-base-en), similarity mechanism (cosine over 500-char overlapping windows), the 200K character cap with truncation flagging, and the exact return format (passages with offsets and similarity scores). No contradiction with readOnlyHint or other annotations.

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 five sentences, each earning its place: purpose, examples, usage, integration, and technical details. It is front-loaded with the core action. Slightly longer than the minimal two-sentence ideal, but every word contributes and there is no 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?

With no output schema, the description fully explains what to expect (top-N passages with offsets and similarity scores). It also covers operational limits (truncation), mechanics (embeddings/windows), and the relationship to sibling tools, making the tool self-contained and 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?

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that 'text' is content already pulled from elsewhere, provides query examples, and explains the 'limit' via 'top-N' and windowing details. This enriches parameter meaning 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 opens with a specific verb+resource ('Semantic search INSIDE a fetched record') and names concrete examples (SEC 10-K, article). It distinguishes itself from siblings by explicitly pairing with ask_pipeworx_grounded and clarifying that it operates on already-fetched text rather than fetching or whole-document grounding.

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 gives explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt.' It also outlines a workflow with ask_pipeworx_grounded (fetch first, then ground over relevant passages), providing clear context for choosing this tool over 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

B3.3/5.0
Disambiguation2/5

The five Zoho CRM tools are well-differentiated, but the 31 Pipeworx/Polymarket tools create heavy overlap (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research all serve similar lookup purposes). Mixing two unrelated domains makes it easy for an agent to select a tool from the wrong group.

Naming Consistency2/5

The Zoho tools share a consistent zoho_ prefix, but the remaining 31 tools follow no clear convention—ask_pipeworx, bet_research, deep_research, entity_profile, forget, scan_dependency, etc. mix noun-first, verb-first, and bare verb patterns without a unifying scheme.

Tool Count2/5

36 tools is well beyond what a Zoho CRM server needs, and only 5 actually relate to Zoho CRM. The bulk are for unrelated data sources, prediction markets, memory management, and web generation, making the set feel bloated and unfocused.

Completeness2/5

The Zoho CRM surface includes create, get, list, and search, but omits essential operations like update, delete, and upsert. The other 31 tools cover a completely different domain, so the server fails to provide complete lifecycle coverage for its stated purpose.

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