parse_semverParse a semantic-version string into major/minor/patch/prerelease/build (keyless, offline). Accepts an optional leading "v".
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes | e.g. "1.2.3-beta.1+build.5". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "version": "1.2.3"
+ },
+ {
+ "version": "v2.0.0-alpha.1+build.123"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds behavioral specifics: 'keyless, offline' (no external dependencies) and 'accepts an optional leading v', which enrich the agent's understanding beyond annotations. No contradictions.
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, concise sentence that front-loads the core functionality. Every word adds value, with no redundancy or irrelevant details.
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 tool's simplicity, annotations cover safety and idempotency, and the description explains the parsing output components (major/minor/patch/prerelease/build). No output schema exists, but the description sufficiently implies the return structure. The tool is fully specified for an agent to understand and invoke correctly.
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% and the schema description provides an example. The tool description adds the critical detail that an optional leading 'v' is accepted, which is not mentioned in the schema description. This adds meaningful information beyond the schema.
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 the tool's function: parsing a semantic-version string into major/minor/patch/prerelease/build components. It uses a specific verb ('Parse') and resource ('semantic-version string'), and the result is well distinguished from sibling tools like compare_semver or satisfies_range.
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 implies when to use the tool (when you need to decompose a version string) but does not provide explicit guidance on when not to use it or how to choose among sibling tools like compare_semver or satisfies_range. There are no alternative suggestions or exclusions.
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.