Gluecron

gluecron_security_scan

Read-only

Everything Gluecron actually knows about a repository's security, in one honest call: OSV.dev CVE/GHSA advisories against the indexed dependency graph (with FIXED VERSIONS), Gluecron's supplemental advisory list, a full-tree committed-secret scan, and optionally a Claude semantic code review. Unlike gluecron_repo_health, an empty result is NEVER reported as a pass: every category returns status 'assessed' | 'partial' | 'not_assessed' | 'error' with a machine-readable reason code, so you can tell 'we checked and it is clean' from 'we never looked'. In particular, a repo whose dependency graph was never indexed returns dependency_advisories.status='not_assessed' (reason 'dependency_graph_not_indexed'), not an empty advisory list. Read-only.

Input Schema

TableJSON Schema

Name	Required	Description
`ref`	No	Branch, tag or commit SHA to scan the tree of. Default: the repo's default branch. Note advisories describe the INDEXED dependency graph, whose commit is reported separately — if it differs from this ref the advisory answer is downgraded to 'partial'.
`repo`	Yes	Repo name
`owner`	Yes	Repo owner username
`include_ai_review`	No	Opt in to the Claude Sonnet semantic review (SQLi/XSS/SSRF/authz/crypto). Costs an Anthropic call and adds seconds. Default false. When it cannot run — no key, provider error, unparseable output — the category reports 'not_assessed', never a pass.
`max_candidate_lines`	No	Cap on lines the secret detector inspects (1-200000, default 20000). The whole tree is always prefiltered; only lines that already matched a secret marker are inspected, so this rarely binds. When it does, committed_secrets reports 'partial' with the exact fraction.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses honest-coverage semantics: status values 'assessed', 'partial', 'not_assessed', and 'error', a machine-readable reason code, and the specific not_assessed case for unindexed dependency graphs. It also explains that AI review failure is reported as 'not_assessed', never as a pass. This is substantial behavioral transparency beyond the annotations.

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 dense but every sentence earns its place: it establishes scope, enumerates security categories, defines the honest status semantics, gives a concrete edge case, and notes read-only behavior. It is front-loaded with the tool's purpose and avoids filler.

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?

Despite lacking an output schema, the description tells the agent exactly what categories will be returned, what statuses can appear, and how to interpret ambiguous cases like unindexed dependency graphs or a bound secret-scan line cap. This is sufficient for an agent to call the tool and reason about its results effectively.

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 schema already documents ref, include_ai_review, max_candidate_lines, owner, and repo in detail. The top-level description adds overall result semantics but does not add parameter-specific meaning beyond what the schema already provides.

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 this tool is a repository security scan and enumerates exactly what it covers: OSV.dev advisories, supplemental advisories, committed-secret scan, and optional semantic AI review. It also distinguishes itself from gluecron_repo_health with a concrete behavioral difference, so an agent can tell which tool to pick.

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 strong contextual guidance by contrasting with gluecron_repo_health and explaining that empty results are never reported as passes. It clarifies when the AI review is optional and how to interpret non-indexed dependency graphs, but it does not state broader when-not-to-use conditions or other alternative 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

B3.4/5.0

Disambiguation2/5

Several tools have near-identical purposes, such as `gluecron_read_file` and `gluecron_repo_read_file` (both read a file from a repo), and `gluecron_explain_repo` and `gluecron_repo_explain_codebase` (both return cached AI explanation). This creates ambiguity despite minor differences in description. While many tools are distinct, the overlapping pairs force an agent to choose between effectively equivalent operations, lowering disambiguation.

Naming Consistency4/5

All tools use the `gluecron_` prefix followed by a verb_noun pattern (e.g., `acquire_lease`, `create_issue`, `merge_pr`). A few tools like `gluecron_ai_cost_summary` and `gluecron_repo_explain_codebase` deviate slightly but remain readable and predictable. Overall, the naming convention is largely consistent, making it easy to infer tool function from the name.

Tool Count2/5

With 60 tools, the server far exceeds the 25-tool threshold for 'too many' per the guidelines. Although the server covers a broad developer platform (repository management, issues, PRs, workflows, AI features, etc.), the sheer number of tools makes navigation heavy and risks overwhelming both agents and users. A more focused set would improve coherence.

Completeness5/5

The tool set is remarkably thorough, covering nearly every lifecycle stage for repositories, issues, pull requests, workflows, branches, commits, and AI-assisted features (chat, test generation, release notes, refactoring, voice-to-PR). Essential CRUD operations are present, and advanced operations like leasing, sandbox provisioning, and multi-repo refactoring are included. There are no obvious gaps for the stated purpose of a developer platform.

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