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inspect_requests

Read the project's request inspector: the most recent requests that hit the mock API (method, path, query, status, origin, captured headers incl. x-* — authorization redacted to its scheme — and a body snippet for writes). Use it to VERIFY what your app / tests / webhook sender actually sent: point code at the mock, run it, then inspect. Pairs with custom_route catch-all bins (e.g. /webhooks/*) for webhook payload + signature debugging. Requires the adminKey, except project "demo" whose inspector is public.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNewest N entries to return (default 20, max 50).
projectYesProject id.
adminKeyNoProject adminKey (not needed for demo).

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden of behavioral disclosure, and it delivers: it discloses that authorization is redacted to its scheme, that only a body snippet is shown for writes, that adminKey is required except for the public 'demo' project, and that the data is the most recent requests. This is strong transparency for a read-only inspector. It could have added details about empty-result behavior or lack of side effects, but the 'Read' framing implies non-mutating.

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 composed of four purposeful sentences: the core definition, the verify workflow, the pairing with custom_route, and the adminKey note. It is densely informative without being padded, and the most important purpose is front-loaded. It earns a 4 rather than a 5 because the density is high and the flow could be slightly tighter, but every sentence contributes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description compensates by enumerating the returned fields (method, path, query, status, origin, captured headers, body snippet). It also covers auth requirements, a usage workflow, and a companion tool for a key use case. Minor gaps remain: no mention of empty result behavior, ordering, or the default/max limit (though limit is in the schema). Overall, it is sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add meaningfully beyond the schema: the adminKey exemption for 'demo' is already stated in the schema ('not needed for demo'), and project and limit are self-explanatory in both places. The description's extra details about captured fields relate to return data, not to parameter semantics. Therefore it meets the baseline but does not exceed it.

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 a specific verb and resource: 'Read the project's request inspector' and enumerates exactly what is returned (method, path, query, status, origin, headers, body snippet). It positions the tool distinctly from its siblings by framing it as the mock-API request log viewer, which no other sibling name suggests. Clear, specific, and differentiates from surrounding tools.

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 when to use it: 'Use it to VERIFY what your app / tests / webhook sender actually sent' and provides a mini-workflow: point code at the mock, run, inspect. It also notes a companion tool, custom_route, for webhook payload and signature debugging. However, it does not explicitly name alternatives to avoid (e.g., query_records for stored data), so it stops short of a full when-not-to-use statement.

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

A4.2/5.0
Disambiguation5/5

Every tool targets a distinct resource or action: project creation, data seeding, record CRUD, traffic inspection, snapshots, and monitoring are all clearly separated. The four monitoring-related tools are carefully differentiated with cross-references, so an agent is unlikely to misselect.

Naming Consistency3/5

Most data and lifecycle tools follow a clear verb_noun pattern (add_resource, create_project, query_records, write_record), but several tools use noun phrases instead (heartbeat, snapshots, project_info, uptime_monitor, custom_route). The split is readable but not a consistent convention.

Tool Count5/5

14 tools is a reasonable, well-scoped size for a combined mock-API platform and monitoring utility. Each tool has a distinct job, and the monitoring tools complement the mock-API lifecycle tools without feeling redundant.

Completeness4/5

The toolset covers project creation/deletion, resource seeding, record CRUD, request inspection, snapshots, and external API monitoring. Minor gaps exist: resources can be added but not individually removed/updated, and custom routes have no delete or update path.

Resources