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Glama

YouSpot

by com.youspot

Ask about invitations

ask_about_invitations
Read-only

Ask a question about the user's LinkedIn invitations — the requests they sent and received. This is the only tool that can see invitations. Use it for questions like 'which invitations I sent were never accepted?', 'who invited me recently?', or 'how many requests did I send last month?'. Acceptance is inferred by checking whether the other person now appears among their connections. Note the LinkedIn export only covers recent and still-pending invitations, not lifetime history — say so when it matters. Returns columns and rows. Keep the question under 500 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe question about their invitations, in plain English.

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 valuable behavioral detail beyond the readOnlyHint annotation: acceptance is inferred via connection appearance, the export covers only recent/pending invitations rather than lifetime history, the tool returns columns and rows, and questions must be under 500 characters. These are important operational nuances an agent needs to set expectations and interpret results.

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 compact and every sentence earns its place: purpose, uniqueness, examples, inference note, data limitation, output shape, and input constraint. It is front-loaded with the core purpose and uses examples efficiently.

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 single-parameter tool with no output schema, the description is fully self-sufficient. It explains what the tool does, how to use it, what behavioral caveats apply, what kind of output to expect, and the input constraint. Nothing essential is missing.

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?

The schema already documents the single 'question' parameter with 100% coverage, so the baseline is 3. The description adds meaningful extra semantics: the question must be about invitations, should be phrased in plain English, and must stay under 500 characters. This goes beyond the schema and helps the agent formulate valid 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 purpose: asking questions about the user's LinkedIn invitations, specifically requests sent and received. It also distinguishes itself by stating 'This is the only tool that can see invitations,' which separates it from siblings like ask_about_connections.

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 explicit example questions and states when the tool should be used ('Use it for questions like...'). It doesn't explicitly name alternatives or spell out when not to use it, but the 'only tool that can see invitations' statement makes the usage context strong and clear.

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