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Glama

YouSpot

by com.youspot

What needs attention

what_needs_attention
Read-only

What this user should deal with right now, computed from their sent mail, their calendar and their LinkedIn export: people who went quiet after real correspondence, people they are meeting this week, who they write to most, invitations nobody answered, and the shape of their network. Each item carries the reason and where the numbers came from. Call this for 'what needs my attention', 'who am I forgetting', 'who should I follow up with', 'what should I do today' and anything else that asks who to deal with without naming one person. Then show an attention_list card and never write the items out yourself. readiness says whether the mailbox has finished being read: when it is 'pending' or 'partial', say so rather than presenting a short list as the whole picture.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindsNoRestrict to certain kinds. Omit for everything, which is almost always right.
limitNoHow many items to return (default 5, max 25).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

The description explains the tool computes results from multiple data sources, that each item carries a reason and provenance, and that readiness indicates mailbox ingestion state. The readOnlyHint annotation already covers safety, and the description adds valuable behavioral context about not presenting partial results as the whole picture.

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 dense but purposeful: it front-loads what the tool computes, then gives call conditions and presentation instructions. A couple of clauses are slightly long, but every sentence earns its place and no content is redundant with the schema.

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 aggregation tool with no output schema and two optional parameters, the description fully covers invocation strategy, output expectations, and caveats about readiness. The instruction to never write the items out yourself is especially important and fills a real usage gap.

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 both parameters. The description adds useful guidance that omitting 'kinds' is 'almost always right' and that limit defaults to 5 with max 25, supplementing the schema with decision-making context.

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 verb and resource ('what this user should deal with right now') and lists concrete computed signals (dormant correspondents, upcoming meetings, unanswered invitations). It also explicitly names query phrasings the tool should handle, distinguishing it from generic follow-up or calendar tools.

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?

It gives explicit call conditions ('call this for...') and explicitly says to show an attention_list card and never write the items out yourself. It also tells the agent how to handle 'pending'/'partial' readiness, which is strong when-to-use and how-to-present guidance.

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