touch_commitmentMark progress on a commitment without changing its status - refreshes last_touched_at_ms so the sweeper distinguishes progressing from abandoned promises.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesAdded value: +falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate idempotent=true and not read-only. Description adds value by specifying the exact field updated (last_touched_at_ms) and the behavioral effect on the sweeper. No contradiction with annotations; consistent with mutation and safe repetition.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence front-loads the action, purpose, side effect, and rationale. No wasted words; every part is informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter heartbeat tool with no output schema and good annotations, the description covers purpose, effect, and why it exists. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no parameter description in schema). Description does not define the parameter 'id', but given the tool's purpose, it is obviously the commitment ID. This is minimally adequate but relies on inference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action: 'Mark progress on a commitment without changing its status' and specifies the exact effect (refreshes last_touched_at_ms). It distinguishes from siblings like update_commitment (which changes status) and commitment_sweep (which cleans up abandoned ones).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides clear context: used for heartbeat/active progress tracking, explicitly says 'without changing its status' (exclusion), and explains why (sweeper distinguishes progressing from abandoned). However, it does not explicitly name alternative tools for status changes, though context implies update_commitment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, with detailed descriptions differentiating them. However, the high count (60) introduces some overlap among memory-related tools (e.g., recall_nearby_memories, search_memories, recent_memory) and environment inspection tools (look_around, look_at, survey_site), causing minor ambiguity.
The majority of tools follow a consistent verb_noun pattern (e.g., enter_space, create_commitment, recall_nearby_memories). A few names break pattern, like cognitive_boot (adjective_noun) or who_is_here (phrase), but overall the naming is predictable and readable.
With 60 tools, the server exceeds the high end of the typical well-scoped range (3-15). While the domain is complex, many tools could be consolidated (e.g., multiple memory retrieval and building tools), making the surface feel bloated rather than lean and focused.
The tool set covers the core functionalities of the 3D world—spatial navigation, building, memory, commitments, skills, and social interaction—with few obvious gaps (e.g., no direct region deletion or agent interaction beyond chat). The breadth is appropriate for the domain, though some redundancy suggests minor over-engineering.