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valem

by io.github.vlad-public-code

Pair with browser

pair_browser

Pair this MCP session with a browser tab on the hosted Valem sandbox so both drive the same live model session. Mints a pairing on first call (or resumes an existing not-yet-approved one) and waits up to a minute for the developer to approve it. Returns {status:"paired"|"already_paired", namespaceId} once done, or {status:"pending", verificationUri, verificationUriComplete, userCode, expiresInSec} if the developer hasn't approved yet — show them verificationUriComplete when present (it already carries the confirmation code, so they only click Approve) and mention that the code on that screen should read userCode; fall back to verificationUri, which requires them to TYPE userCode. Then call this tool again (it resumes the SAME pairing, it does not mint a new one). Every other model tool (create_model, mutate, evolve_spec, get_state, explain, ...) fails with a clear error until pairing succeeds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description reveals non-obvious behavior beyond the annotations: it mints versus resumes a pairing, waits up to a minute for approval, returns different statuses, and blocks other tools until success. This is rich behavioral context that annotations alone do not provide.

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?

Although the description is long, the pairing flow is genuinely complex and every sentence earns its place: purpose, timeout behavior, return shapes, user-facing instructions, fallback handling, and repeat-call semantics. There is no filler or repetition.

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?

With no output schema, the description fully documents the possible return shapes and the required follow-up actions. It also covers failure behavior for sibling tools, making it complete for an agent to invoke and interpret this tool correctly.

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?

The tool has zero parameters and the schema is effectively fully covered, so there is no parameter ambiguity. The description focuses on behavior and return values, which is appropriate given the empty input 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 states a specific action ('Pair this MCP session with a browser tab'), identifies the target resource ('hosted Valem sandbox'), and explains the purpose ('so both drive the same live model session'). It is clearly distinct from the sibling model-manipulation tools.

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 establishes this tool as a prerequisite: 'Every other model tool ... fails with a clear error until pairing succeeds.' It also tells the agent exactly what to do after a pending response: call the tool again, and that the second call resumes the same pairing rather than minting a new one.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.

Resources