list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context about default behavior (active only) and return fields, which is helpful but not contradictory.
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?
Two sentences efficiently convey purpose, return fields, and usage guidance. No redundant or irrelevant information.
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 the tool's simplicity (one optional parameter, no nested objects, clear annotations), the description covers purpose, usage, and return fields sufficiently. No missing critical information.
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 100% for the single parameter include_inactive, which is documented in the schema. The description implies the default (active) but doesn't add significant new meaning beyond the schema, so baseline 3 is appropriate.
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 explicitly states 'List the caller's active subscriptions' and lists the return fields, clearly identifying the verb (list) and resource (subscriptions). It distinguishes from siblings like subscribe/unsubscribe.
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 provides clear when-to-use context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It lacks explicit when-not-to-use or alternative named tools, but given the siblings, it's adequate.
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.
Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer broad data questions with only subtle differences, and the Polymarket suite (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) has blurry boundaries. An agent could easily misselect among these without reading the full descriptions.
There are recognizable prefixes like polymarket_*, rates_by_*, and ask_pipeworx_*, but the set mixes verb_thing, thing_verb, and bare-noun names (deep_research, entity_profile, validate_claim, remember, generate_llms_txt, suggest_questions). The conventions are readable but not uniform across the full surface.
33 tools is above the threshold for a focused server, and the server name 'Gsa Perdiem' suggests a tiny domain that only two of the tools (rates_by_city, rates_by_zip) actually serve. Even for the broad Pipeworx platform scope, the many near-duplicate meta-tools and overlapping prediction-market tools make the count feel bloated.
The broad data-research surface is fairly complete: entity resolution, profiling, changes, grounded Q&A, memory CRUD, subscriptions, and prediction-market analysis all have coverage. However, the per-diem domain implied by the server name is under-served (no state-level lookup, no fiscal-year selection), and the mix of two domain-specific tools with a sprawling platform creates obvious gaps relative to the stated identity.