Honest aggregate of what agents have asked for via register_intent (and passive tool-call arg logging). 0 entries = returns 0. No fabrication. LIVING GATE posture: empty is empty. Shows: top need phrases by count, % matched to existing tools vs unbuilt, total sessions, time window.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, so the description's emphasis on 'honest aggregate' and 'no fabrication' adds context about truthfulness of empty results. It explains the data source and that empty means empty, which are behavioral traits beyond annotations.
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?
The description is fairly concise and front-loaded with the core purpose. It contains no unnecessary words, though it could be slightly shorter by removing redundant emphasis on honesty. Still efficient.
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?
Given no parameters and no output schema, the description explains the output fields (top need phrases, counts, percentages, total sessions, time window) sufficiently. It does not mention sorting or pagination, but for a read-only aggregate, it is complete enough.
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?
The tool has zero parameters, so baseline is 4. The description correctly omits parameter details as none exist.
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?
The description clearly states that the tool aggregates what agents have asked for via register_intent and passive logging. It specifies verb 'aggregate' and resource 'demand/needs', and lists output fields (top need phrases, counts, percentages). It implicitly distinguishes from sibling register_intent (which registers) and other analytics tools.
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?
The description implies usage for checking demand for agent requests, but does not explicitly state when to use this tool versus alternative analytics tools like analytics_next_steps or capability_landscape. No when-not or alternative guidance is provided.
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.
With 94 tools spanning overlapping concepts (multiple readiness/audit/grade tools, many status checkers, deprecated aliases like trust_* vs observation_*), agents will frequently struggle to pick the right one. While each tool is individually distinct, the sheer volume and conceptual overlap (e.g., audit_mcp, readiness_report, verify_mcp_ready, lookup_readiness, recommend_mcp, subscribe_grade_changes) create high misselection risk.
Most tools use snake_case with underscores, but the pattern is inconsistent: some are verb-first (audit_mcp, verify_mcp_ready, claim_start, check_engagement) while others are noun-first (receipt_issue, meter_open, work_order_open, agent_invoice_status). Deprecated aliases like trust_compare vs observation_compare further break consistency, though the majority remain readable.
94 tools is far beyond any reasonable scope for a single server, even one with broad ambitions like 'observatory + town'. The calibration notes 50+ as extreme mismatch; this server far exceeds that. Many tools are highly specific (e.g., factory_resolve_dead_letter, visit_touch_status, start_here) and could be consolidated or split into separate servers.
The server covers a wide range of domains (auditing, claiming, receipts, meters, escrow, work orders, gold rush, town, analytics) and offers many CRUD-like operations, but several lifecycle gaps exist: no cancel/close for work orders (only open/accept/deliver/accept_delivery), escrow (only open/attest/status), or meters (only open/charge/status). Given the massive scope, important operations are missing, though core workflows are present.