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Glama

TeamShared

by io.github.xhad

Health

health

Liveness + dependency probe.

Returns {"status", "version", "components": {server, redis, postgres, semantic, distiller, graph, ollama}}. semantic is the pgvector + embedder store. Optional deps report "disabled" when off and do not degrade overall status. Always cheap; safe to poll on a 10s interval. Used by Docker healthcheck and the /health HTTP route.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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?

With no annotations provided, the description carries the full behavioral burden and meets it thoroughly. It discloses that optional dependencies report 'disabled' when off without degrading overall status, defines what 'semantic' means, and states the probe is cheap and safe to poll—operational behavior beyond a generic liveness check.

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?

Every sentence earns its place: purpose, return shape, component clarification, disabled-dependency behavior, and operational guidance. The description is compact, front-loaded, and completely free of filler.

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 zero-parameter liveness probe with an output schema, the description covers the return contents, disabled-dependency behavior, cost/rate expectations, and common usage context. Nothing an agent needs to invoke and interpret the tool correctly 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?

This tool has zero parameters and the schema already documents that with 100% coverage, so the baseline is 4. There is no parameter-level meaning to add, and the description instead clarifies the return fields, which is the more relevant semantic content here.

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 'Liveness + dependency probe', a specific noun phrase naming the resource and its purpose. It details the returned status, version, and component map, and references the '/health' HTTP route and Docker healthcheck, making it clearly distinct from siblings like 'version'.

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 this tool is used by Docker healthcheck and the '/health' HTTP route, and says it is 'always cheap; safe to poll on a 10s interval'—clear context for when to use it. It does not name alternatives or a when-not-to-use condition, so it falls just short of a 5.

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

B3.4/5.0
Disambiguation4/5

With 104 tools across many domains (memory, work, projects, files, agents, context, strategic, ontology), the use of clear prefixes (memory_, work_, project_, file_, agent_run_, context_) makes most tools distinct. However, there are some potential confusions between memory_session_* vs memory_state_*, and memory_recall vs memory_think vs memory_assemble_context, though descriptions clarify their specific purposes. Aliases like memory_playbook_get for memory_procedure_get are explicit and reduce ambiguity.

Naming Consistency5/5

Tool names follow a highly consistent pattern: prefix_domain_action (e.g., file_create, work_update, memory_recall, agent_run_start). All use snake_case, with verbs consistently placed after the domain prefix. Even less common tools like account_brief and attention_snapshot fit the overall naming scheme, making the set predictable and easy to navigate.

Tool Count3/5

At 104 tools, this is an exceptionally large surface area, far exceeding the 25+ threshold that feels heavy. However, the server covers an extensive domain (organizational memory, work management, project tracking, file sharing, agent orchestration, and strategic planning), which justifies a large count. Still, the sheer number may overwhelm agents, and some tools could be consolidated (e.g., many memory_session_* and memory_state_* variants).

Completeness4/5

The tool surface is remarkably complete for its stated purpose, covering CRUD operations for files, work items, projects, and memory, plus lifecycle management for agents, sessions, and strategic plans. Minor gaps exist (e.g., no direct memory_item_get by ID, no section removal in projects), but agents can work around these using existing tools like memory_recall or work_create with parent_id. Overall, the set minimizes dead ends.

Resources