Gluecron

gluecron_get_diff

Read-only

Read the actual changes in a commit or a branch range. Pass sha for one commit against its first parent, or base+head for everything head adds since the merge base (what a PR proposes). Returns {files:[{path, oldPath?, status, additions, deletions, binary, patch, patchTruncated}], stats, truncated}. Patch text is byte-capped; set include_patch=false for a files-changed summary only.

Input Schema

TableJSON Schema

Name	Required	Description
`sha`	No	Commit SHA — diffs it against its first parent
`base`	No	Base ref (with `head`)
`head`	No	Head ref (with `base`)
`repo`	Yes
`owner`	Yes
`include_patch`	No	Include patch bodies (default true)
`max_patch_bytes`	No	Total budget for patch text across all files (default 400000, max 2000000)

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral detail: it documents the return object shape, the `truncated` flag, and that patch text is byte-capped. This is valuable context beyond the structured fields and is consistent with the read-only 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?

Three focused sentences with zero fluff: purpose, invocation modes, return shape, and truncation caveat are all front-loaded and each sentence earns its place.

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?

With no output schema, the description wisely includes the return shape and truncation semantics. It covers both call modes and the summary-only switch. A minor gap is that it doesn't explicitly state `sha` and `base`+`head` are mutually exclusive, though `or` implies it.

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 coverage is 71%, already covering most parameters, and the description adds the key relational semantics between `sha` vs `base`+`head`, plus the effect of `include_patch=false`. It doesn't detail `max_patch_bytes`, but that is already defined in the schema.

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 actual changes in a commit or a branch range.' It clearly differentiates the tool from siblings like gluecron_get_commit or gluecron_get_pr by focusing on diff content and explicitly naming two diff modes.

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?

It gives concrete usage conditions: pass `sha` for one commit against its first parent, or `base`+`head` for the PR-style branch range. It doesn't name explicit alternatives or exclusions, but the context is clear enough for an agent to decide when to invoke it.

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.

Resources

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