satisfies_rangeTest whether a version satisfies a range: exact, ^ (caret), ~ (tilde), comparators (>=,>,<=,<,=), x-ranges (1.2.x), AND (space-separated), OR (||). E.g. version "1.4.2", range "^1.2.0".
| Name | Required | Description | Default |
|---|---|---|---|
| range | Yes | The range, e.g. "^1.2.0" or ">=1.2.0 <2.0.0". | |
| version | Yes | The version to test. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "range": "^1.2.0",
+ "version": "1.4.2"
+ },
+ {
+ "range": ">=1.0.0 <2.0.0",
+ "version": "1.5.0"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds detailed behavioral context by enumerating supported range syntaxes (caret, tilde, comparators, x-ranges, AND, OR) and providing an example, which is beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with an example, front-loading the purpose. Every element earns its place; there is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (2 required params, no output schema), the description is nearly complete. It could optionally mention that the return is a boolean, but the purpose 'test whether' implies a boolean result. The examples cover typical usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both parameters. The description adds extra value by explaining the range syntax with examples, which enhances the agent's understanding beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Test whether a version satisfies a range', a specific verb+resource. It distinguishes from sibling tools like 'compare_semver' and 'parse_semver' by focusing on satisfaction testing, and provides examples of supported syntax.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use this tool (version range testing) but does not explicitly mention when not to use it or list alternatives from siblings. The context is sufficient for correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several clusters overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve general data-query purposes, and entity_profile, compare_entities, recent_changes, and validate_claim pull from the same SEC/news/data sources in similar ways. The semver utilities are distinct, but they sit alongside unrelated prediction-market, memory, subscription, and AI-visibility tools that make the overall boundary of each tool much fuzzier.
Some tools follow a clean verb_noun pattern (parse_semver, compare_semver, compare_entities, resolve_entity), but others are noun phrases (entity_profile, polymarket_edges, recent_changes) or branded/verb-first names (ask_pipeworx, deep_research, bet_research, pipeworx_trending). The mix is readable but inconsistent, with no unifying convention across the 34 tools.
34 tools is too many for a server named Semver, whose actual semver-related surface is only a few utilities. Even viewed as a broad data platform, the count is heavy and padded with unrelated capabilities like prediction-market arbitrage, memory storage, subscriptions, and AI-visibility checks that do not belong together in one server.
As a Semver server it covers parse, compare, and range satisfaction but lacks obvious operations like version bumping/incrementing or validating a version list, making the core surface incomplete. As a general data platform the domain is unclear and the unusual mix of semver, market, memory, and marketing tools prevents any coherent completeness assessment.