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Glama

Layero

by ru.layero

Замерить скорость сайта

check_performance
Read-only

Measure a site's speed and say whether it got worse after a deploy.

The verdict comes FROM THE SCORE, not from timings: repeated runs of the
same deploy vary by at most one point, while LCP and TTFB on identical
code swing by 37-40%. A difference of 3 points or more is meaningful;
anything smaller is measurement noise, and calling that a regression is
inventing a problem.

A run takes tens of seconds. With `wait=true` the tool waits for you; do
not start a second measurement in parallel — it will occupy the queue
and add nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoДождаться результата замера (десятки секунд) или вернуться сразу. `false` — когда пользователю нужен не результат, а сам факт запуска.
projectYesПроект: слаг (`my-site`) или id. Слаг — то, что видно в адресе сайта; если пользователь назвал сайт словами, возьми слаг из `my_projects`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
deltaYes
scoreYes
messageYes
projectYes
timingsYes
verdictYes
next_actionYes
previous_scoreYes

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / project / description
      Added value: +"Проект: слаг (`my-site`) или id. Слаг — то, что видно в адресе сайта; если пользователь назвал сайт словами, возьми слаг из `my_projects`."
    • addedInput schema / properties / wait / description
      Added value: +"Дождаться результата замера (десятки секунд) или вернуться сразу. `false` — когда пользователю нужен не результат, а сам факт запуска."
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations only provide readOnlyHint=true. The description adds substantial behavioral context: the verdict is based on a score, not raw timings; repeated runs vary by at most one point while LCP/TTFB swing 37-40%; a 3-point difference is meaningful; run duration is tens of seconds; wait=true behavior and queue effects are disclosed. This goes far beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, front-loaded with the core purpose. Every sentence provides necessary information: the noise threshold, run duration, wait behavior, and parallel warning. No filler or redundancy.

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?

For a performance measurement tool with an output schema, the description fully covers the key contextual aspects: why the score matters over timings, what constitutes a meaningful regression, how long a run takes, and how wait/parallel behavior works. The output schema likely documents return values, so no need to repeat them here. This is a complete and self-contained description.

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 coverage is 100%, so both parameters are already documented with descriptions. The description reinforces the wait semantics and mentions deriving project slug from my_projects, but this largely duplicates the schema. It adds the queue caveat, which is more behavioral than parameter-specific, so the marginal added value is limited.

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 clearly states the tool's function: 'Measure a site's speed and say whether it got worse after a deploy.' This is a specific verb+resource combination that distinguishes it from sibling tools like site_status (status) and diagnose_deploy (diagnose). The deploy-regression context is explicit.

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 provides a clear usage context: after a deploy, to detect regressions. It also gives an important usage constraint: do not start a second measurement in parallel because it will occupy the queue. It does not explicitly name alternatives, but the deploy-specific framing implies when to use it versus other tools.

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

A3.8/5.0
Disambiguation5/5

Each tool targets a distinct resource and action. The deploy lifecycle is cleanly separated into cancel, retry, rollback, diagnose, logs, list, and status, while the site_* tools each answer a different question. Even the two compose_landing tools are explicitly differentiated for model vs. internal use.

Naming Consistency3/5

Tool names mix verb-first patterns (check_domain, list_deploys, connect_analytics) with noun-first patterns (site_issues, deploy_logs, env_vars), and some are bare verbs (rollback, whoami). The naming is descriptive and readable, but not consistent enough to predict the style for a new tool.

Tool Count2/5

27 tools exceeds the typical well-scoped range and pushes into 'too many' territory. While the server covers a broad platform scope, many tools are highly specialized (check_copy, site_screenshot), and an agent may be overwhelmed by the sheer number of choices. Consolidation could reduce the load.

Completeness4/5

The core lifecycle is solid: compose, publish, monitor, diagnose, and rollback, with supporting tools for domains, analytics, performance, and content inspection. However, there are no delete/remove operations for projects, domains, or integrations, and integration management is limited to adding. These are minor gaps that agents can work around, but they are notable for a full platform.

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