KernelScan

update_product

Update a product owned by the caller. Re-runs analysis if the kernel_version, arch, or referenced .config changed.

    To change the .config, first POST the new file to
    ``/api/configs/uploads`` (see ``create_product`` for the curl recipe)
    and pass the returned ``config_upload_id`` here. Leave
    ``config_upload_id`` as ``None`` to keep the existing .config.
    ``factor_ids=None`` leaves factor selections untouched; an empty
    list clears them. Same tier gates as PUT /api/products/{id}.

    A change that re-runs analysis (``kernel_version``, ``arch``, or the
    ``.config``) spends one unit of the team's shared monthly analysis
    allowance and can fail with the same durable "Monthly analysis limit
    reached … [429]" quota error as ``create_product`` (distinct from the
    transient rate-limit 429 — don't retry it). A rename / description /
    factor-only edit runs no analysis and is free.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`arch`	No
`name`	No
`factor_ids`	No
`product_id`	Yes
`description`	No
`kernel_version`	No
`config_upload_id`	No

Schema Changelog

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

First observedJun 27, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: it spends one unit of the shared monthly analysis allowance on re-running analysis, can fail with a durable 429 quota error distinct from the transient rate-limit, and explicitly says 'don't retry it'. It also explains the semantics of config_upload_id and factor_ids, covering side effects and edge cases.

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 but well-structured, with each paragraph addressing a distinct aspect (basic behavior, config upload, cost/error handling). Every sentence adds value, and it is front-loaded with the primary purpose. It is longer than a minimal description but appropriately so given the tool's complexity.

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 7-parameter mutation tool with no annotations and no output schema, this description is remarkably complete. It covers authorization ('owned by caller'), quota impact, error differentiation, parameter semantics, and references the API route. No important aspect of tool behavior is left unexplained.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates for the non-obvious parameters: config_upload_id (upload flow, None keeps existing), factor_ids (None vs empty list), and kernel_version/arch (trigger analysis). Name, description, and product_id are left to the schema but are self-evident. Minor gap: no explicit treatment of name/description, but these are trivial.

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: 'Update a product owned by the caller.' It further clarifies scope by specifying when analysis is re-run (kernel_version, arch, or .config changed), which also distinguishes behavior from the sibling create_product. This makes the tool's purpose unambiguous.

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 gives explicit guidance on when analysis is re-run versus free edits (rename/description/factor-only), and explains the multi-step process for changing the .config via the upload endpoint. It references the API tier gates and create_product for the curl recipe. However, it does not explicitly state 'use this to update an existing product instead of create_product', though it is implied.

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

A4.3/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: product lifecycle (create/get/list/update), CVE lookup/search, VEX retrieval, access request, support submission, and identity/quota check. There is no overlap; even create_product and update_product are clearly differentiated by their operation semantics.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (create_product, get_cve, list_products, etc.) using lowercase with underscores. The only outlier is whoami, which is a standard Unix-style command and fits naturally.

Tool Count5/5

10 tools is well-scoped for the domain of Linux kernel security scanning and CVE/product management. Each tool serves a clear purpose without redundancy or bloat, covering the core workflows without overwhelming the agent.

Completeness4/5

The tool set covers the major lifecycle for products (create, get, list, update) and CVEs (search, get), plus VEX retrieval and account context. Minor gaps exist such as product deletion or direct config upload, but these can be worked around via existing tools and the REST API.

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