Skip to main content
Glama

simulate_fixes

INFLUENCE — project a set of fixes onto a provider's agent-readiness score and band before doing the work. Re-applies the band gate to the PROJECTED state, so it will tell you when buying points still leaves you demoted. Kin Score facets are refused rather than estimated: several checks behind them are count-based across a provider's APIs, so a single claimed fix has no computable composite value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
fixesYesAgent-readiness dimension ids to assume fixed, e.g. ["idempotency","mcp_server"]. what_can_i_fix lists the available ones.
contextNoOptional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses meaningful behavior: the band gate is re-applied to the projected state, and Kin Score facets are refused rather than estimated, with the count-based reasoning explained. It does not explicitly state side effects or write behavior, but 'simulate' and 'before doing the work' imply a non-mutating operation.

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 and front-loaded: the first clause immediately states the core purpose, and each subsequent sentence adds distinct behavioral information. No sentence is wasted, and the length is appropriate for the complexity.

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?

The description explains the key behavioral edge cases—band re-gating and refusal of Kin facets—without an output schema. Return values are inferable from 'project onto score and band' and 'tell you when buying points still leaves you demoted.' It does not specify exact response formatting, but the lack of an output schema is partially mitigated by this inferred contract.

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?

The schema documents 'fixes' with examples and a pointer to what_can_i_fix, and 'context' with a thorough explanation, but 'slug' has no description. The tool description itself adds little parameter-level detail, leaving 'slug' ambiguous. With 67% schema coverage, this is adequate but not complete.

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—projecting fixes onto a provider's agent-readiness score and band—and clarifies it is a simulation before the work is done. It distinguishes itself from actual fix application and from plain readiness lookup by emphasizing the PROJECTED state and re-applied band gate.

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 clearly indicates when to use the tool: before doing the work, to see whether buying points still leaves a provider demoted. It does not explicitly name alternative tools or exclusion conditions, but the usage context is strong enough for an agent to select it appropriately.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation3/5

Most tools are clearly separated by artifact type or resource (find_mcp vs find_openapi vs get_provider vs get_api), but the sheer volume creates some genuinely confusable clusters: apis_io_search vs find_apis vs find_artifacts, and insights_adoption vs insights_dimensions vs find_company_insights. Several readiness-related tools (what_can_i_fix, simulate_fixes, readiness_gates) also share a conceptual boundary, though their descriptions do help.

Naming Consistency3/5

The dominant patterns (find_*, get_*, cohort_*, compare_*) are consistent and predictable, but the set mixes in irregular names like apis_io_search, tag_group_tags, what_can_i_fix, whats_changed, and resolve. These deviations are readable but break the otherwise regular verb_noun convention.

Tool Count2/5

106 tools is far beyond the typical well-scoped server and will impose a heavy selection burden on agents. The server covers a genuinely broad domain (catalog search, ratings, cohorts, agent readiness, lists, exports, feedback), so the count is defensible in scope, but it is still too many to navigate efficiently.

Completeness5/5

The surface is remarkably complete: search and browse, single-entity detail, comparisons, cohort analytics, agent-readiness assessment, saved searches, list management, feedback/correction flows, and full dataset exports are all covered. There are no obvious dead ends, and even minor operations like re-running saved searches or simulating fixes are present.

Resources