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Glama

Correct the questions, set the rescan cadence

update_competitor_scan
Idempotent

Correct a scan still in draft, and set the cadence of one already armed. questions is where the measurement is won: send the questions to KEEP, with the wording you want asked. A question of the scan left out of the list is removed, and the amount announced by estimated_cost_minor follows, so this is also how the person spends less. Nothing is charged until start_competitor_scan, so rewriting costs nothing; a scan already measured keeps its questions (409) and a new one asks something else. rescan_cadence sets how often an armed scan runs again: "weekly" or "monthly" sets it, null stops it. Weekly widens the list faster and spends as often; monthly follows the pace a corpus of third-party pages moves at. Each pass widens the list rather than refreshing it, because two checks of the same question do not cite the same pages, and what one check found the next one keeps. SPENDS on every pass, the same amount as the first one, so name it to the person before setting a cadence. It states a STATE and replays safely both ways: setting the cadence already in place moves nothing, and stopping it leaves the accumulated list intact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsNoThe questions to KEEP, in the order to ask them. A sent list REPLACES the questions of the scan: one left out is removed. Draft only.
rescan_cadenceNoHow often the scan runs again: "weekly", "monthly", or null to stop it.
competitor_scan_idYesThe UUID of the competitor scan: call list_competitor_scans to find it.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate idempotent and non-destructive, but the description adds significant context: replacement semantics for questions, cost behavior per rescan pass, no-op when setting same cadence, and that stopping cadence preserves data. This goes well beyond the structured hints.

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 dense and front-loaded with purpose, but it is quite long. Every sentence earns its place, yet the prose could be tightened without losing meaning. Still, structure is logical: purpose, then parameter-specific behavior, then idempotency/cost notes.

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?

The tool has two distinct modes, cost implications, idempotency behaviors, and edge cases (409, null cadence). The description covers all of these thoroughly, including what happens to the accumulated list when cadence stops. No gaps remain.

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

Parameters5/5

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

Despite 100% schema coverage, the description adds crucial meaning: questions list replaces rather than appends, omitted questions are removed, and cadence choices have strategic trade-offs (weekly spends more, monthly matches corpus pace). It also connects to estimated_cost_minor, providing cross-parameter context.

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 clear verb+resource statement: 'Correct a scan still in draft, and set the cadence of one already armed.' It explicitly distinguishes between two modes and implicitly differentiates from sibling tools like create_competitor_scan and start_competitor_scan.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: draft vs armed scans, and notes that changing questions after measurement returns 409, implying the need to create a new scan instead. It also explains cost implications ('Nothing is charged until start_competitor_scan') and when to set cadence.

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

A4/5.0
Disambiguation4/5

Each tool maps to a distinct resource and action, and the descriptions go out of their way to separate near-neighbor concepts like surfaces vs corroborations and score series vs raw responses. A few related pairs (get_results/get_responses, get_credits/get_usage, create_surface/create_corroboration) could still be confused at a glance, so it is not a perfect 5.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun snake_case pattern across all 67 tools, with clear families like create_, update_, get_, list_, archive_, restore_, and delete_. Minor quirks such as topup_credits as one word do not break the overall uniformity.

Tool Count1/5

67 tools is an extreme count for a single MCP server, even for a broad brand-monitoring domain. The surface is bloated with lifecycle variants per entity, and the sheer number makes the server hard to navigate and prompt against.

Completeness5/5

The server covers full lifecycles for projects, trackers, surfaces, corroborations, quests, logbook entries, keyword discoveries, competitor scans, link targets, sources, support, and billing. Archive/restore and soft-delete paths prevent dead ends, and nearly every obvious workflow has a corresponding tool.

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