io.github.TheTsungYing/annealbridge

get_optimization_capabilities

Discover supported variable types, constraint operators, objective terms, backend availability, and resource limits for optimization problems. Request the full JSON schema when you need more detail.

Instructions

Describe what this optimization server accepts and which solver backends are usable right now.

Call this when you need the supported problem vocabulary (variable types,
constraint operators, objective terms, accepted schema versions) or, per
backend, whether it is installed/configured (available), whether server
policy permits it (enabled), and its resource limits. It is not required
before every problem: for a small binary problem on a local backend the
example in the server instructions already shows the document shape. This
performs no solving and no network requests.

The full problem JSON schema is several times the size of everything
else, so problem_json_schema is null unless include_schema is true. Ask
for it when the document needs more than the example shows — integer
variables, soft constraints, solver preferences — or before inventing a
field; every field description is in it.

supported_variable_types lists the variable types a problem may declare —
"binary" and "integer" — and schema_versions lists every problem schema
version this server accepts, newest last. Integer variables are only
allowed when the problem carries "version": "1.1" at its top level;
"version": "1.0" accepts binary variables only.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`include_schema`	No	Whether to include the full OptimizationProblem JSON Schema as problem_json_schema. The default false keeps the response small; pass true only when the schema itself is needed, e.g. for integer variables or an unfamiliar field.

Output Schema

TableJSON Schema

Name	Required	Description
`backends`	Yes	Every registered backend, with its flags and limits.
`schema_version`	Yes	The newest problem schema version this server accepts; use it unless an older one is needed.
`schema_versions`	Yes	Every accepted problem schema version. Derived from the model, not hard-coded; "1.1" is a superset of "1.0".
`problem_json_schema`	Yes	The full OptimizationProblem JSON Schema, identical to what annealbridge export-schema prints, when it was requested (include_schema on the MCP tool); null otherwise. Its field descriptions explain the document one level down.
`annealbridge_version`	No	The installed package version that produced this view; "unknown" outside an installed distribution.
`supported_variable_types`	Yes	The variable types a problem may declare.
`supported_objective_terms`	Yes	The kinds of objective term a problem may carry.
`supported_constraint_operators`	Yes	The operators a constraint may use.
`inequality_requires_integer_coefficients`	Yes	Whether <= / >= constraints need integral coefficients and rhs, which the slack encoding requires.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 22, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations to rely on, the description carries the full burden and does so thoroughly. It discloses that the tool 'performs no solving and no network requests,' explains that problem_json_schema is null unless include_schema is true, and even surfaces version-specific behavior such as integer variables only being allowed with version 1.1.

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?

Each of the three paragraphs earns its place: purpose and usage, optional-parameter rationale, and version-specific constraints. The content is front-loaded with the core purpose, and no sentence is filler or redundant with the schema.

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?

Given the simple parameter surface and the presence of an output schema, the description covers everything needed: when to call it, what it returns, when to opt into the larger schema, and behavioral guarantees. It also differentiates the tool from the sibling tools without needing to enumerate their schemas.

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

Parameters5/5

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

The schema already documents the single include_schema parameter well, and the description adds further decision-useful meaning: when to pass true (integer variables, soft constraints, solver preferences, unfamiliar fields) and why false is the default (keeping the response small). This goes beyond the schema's own description.

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: 'Describe what this optimization server accepts and which solver backends are usable right now.' It also distinguishes itself from the solving-focused sibling tools by stating 'This performs no solving and no network requests,' so an agent can identify it as a read-only capabilities query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to call the tool ('Call this when you need the supported problem vocabulary...') and when it is unnecessary ('It is not required before every problem'). It also gives precise guidance for the optional include_schema parameter, telling the agent to request the full schema when handling integer variables, soft constraints, solver preferences, or unfamiliar fields.

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

Deploy Server