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Glama

YouSpot

by com.youspot

Search connections

search_connections
Read-only

Search the user's own LinkedIn connections by name, company, or job title. Use this to answer questions like 'who do I know at Stripe?', 'which of my connections are founders?', or to look someone up by name. Returns matching people with their company, title, headline and location. Omit all filters to list the most recent connections. On a miss the response also reports how many connections the user has imported at all: if no_imported_data is set, their connections are simply not loaded — say that, and never conclude from an empty result that they don't know the person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 20, max 50).
queryNoMatch against the person's full name.
titleNoMatch against the person's job title.
companyNoMatch against the company name.

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?

The description adds significant behavioral detail beyond the readOnlyHint annotation: return fields, the effect of omitting filters, and the critical no_imported_data semantics that prevent the agent from making a false negative conclusion. This is exactly the kind of behavioral disclosure agents need.

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 efficient and front-loaded: the core purpose appears first, followed by concrete use cases and a brief return/behavior summary. The no_imported_data warning earns its place because it prevents a serious inference error.

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?

For a read-only search tool with no output schema, the description covers return contents, parameter behavior, edge-case response semantics, and the meaning of empty results. Nothing essential is missing for an agent to call and interpret this 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%, so the schema already documents each parameter well. The description adds value by providing usage examples and clarifying that omitting all filters lists recent connections, which supplements the schema without duplication.

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 states a specific action and resource: 'Search the user's own LinkedIn connections by name, company, or job title.' It includes concrete example questions that distinguish this tool from broader connection-related siblings like get_connections_summary or ask_about_connections, so the agent can identify when this direct search is appropriate.

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 gives clear usage context with example queries and explicitly says to omit filters to list recent connections. It does not explicitly name alternatives or state when not to use this tool, but the context is strong enough for an agent to recognize the intended use.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear boundaries even within overlapping domains like LinkedIn (search vs. free-form query vs. profile vs. summary) and graph deletion (soft single, bulk soft, permanent single). Descriptions explicitly cross-reference related tools to prevent misselection.

Naming Consistency4/5

The vast majority follow a consistent verb_noun pattern (get_, list_, search_, create_, delete_, etc.). A few noun-phrase exceptions like linkedin_analytics, mutual_connections, similar_objects, and what_needs_attention deviate slightly, but they are still descriptive and do not create confusion.

Tool Count2/5

At 66 tools this is far beyond the 25+ threshold considered too many, even though the server covers many integration domains. Each domain has a coherent subset, but the overall surface is heavy for agents to navigate and would benefit from consolidation or namespacing.

Completeness4/5

The set provides deep read/search coverage across Gmail, Slack, Calendar, LinkedIn, HubSpot, Obsidian, Twitter, and a graph store, with core write operations for calendar, drafts, Slack, and graph objects. Minor gaps exist—notably no calendar delete, no direct Gmail send to third parties (only drafts), and no LinkedIn post/message actions—but these appear deliberate and do not block typical workflows.

Resources