Skip to main content
Glama

Codestral fill-in-the-middle completion

codestral_fim
Read-only

Fill in missing code between a given prefix and suffix. Use for editor autocomplete, code patching, or structured refactors where boundaries are specified.

Instructions

Fill-in-the-middle code completion with Codestral.

Given prompt (code preceding the cursor) and suffix (code after the cursor), Codestral writes the middle. Use for editor autocomplete scenarios, code-patching agents, or structured refactors where you know the target boundaries.

Default stop tokens: [] — let the model decide. Override with stop if needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesCode preceding the cursor.
suffixYesCode after the cursor. Can be empty string.
modelNo
stopNo
temperatureNo
max_tokensNo
top_pNo
seedNoRandom seed for deterministic sampling. Maps to Mistral's `random_seed`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
modelYes
finish_reasonNo
usageNo
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds value by explaining the default stop behavior and the ability to override it. No contradictions. Additional context on the completion mechanism ('writes the middle') is provided.

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?

Four sentences with no redundancy. Information is front-loaded: purpose first, then usage, then behavioral tweak. Every sentence adds value.

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?

Given the presence of an output schema and annotations, the description covers the core concept and key behavior of stop tokens. It does not detail all 8 parameters, but the essential ones for operation are explained. The description is sufficient for an AI agent to use the tool 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 only 38%, but the description explains the key parameters prompt and suffix clearly. It also mentions stop default and override. However, common parameters like temperature, max_tokens, top_p are left to the schema, which lacks descriptions. The description does not fully compensate for the low coverage.

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?

Explicitly states 'Fill-in-the-middle code completion' with clear verb-resource pairing. It describes the input structure (prompt/suffix) and output (writes the middle) clearly distinguishing it from sibling tools like chat, OCR, vision.

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?

Lists specific use cases (editor autocomplete, code-patching agents, structured refactors) but does not provide explicit when-not-to-use guidance or compare to siblings. However, the sibling tools are very distinct, making the intended usage clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Swih/mistral-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server