Search Greek job listings on Jobily.gr by role, location, sector or company. Anonymous, read-only.
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Average 4.4/5 across 5 of 5 tools scored.
Each tool targets a distinct resource and action: get_job retrieves details, search_jobs performs queries, list_sectors and lookup_locations provide filter facets, and lookup_roles_and_companies handles role/company suggestions. There is no overlap in purpose, and the descriptions clearly differentiate the roles.
All tool names follow a consistent verb_noun pattern in snake_case (get_, list_, lookup_, search_). Although the verbs vary, the pattern is uniform and predictable, matching the style of well-organized RESTful or MCP naming conventions.
Five tools form a compact, well-scoped set for a job-search read API. This falls comfortably within the ideal 3-15 range, and each tool serves a necessary purpose without redundancy.
The tool surface fully covers the core job-listing workflow: facet discovery (sectors, locations, roles/companies), searching/filtering, and retrieving full details. No missing operations are apparent for a read-only job board; common gaps like update/delete are out of scope for this server.
get_jobGet job detailsAGet the full posting of a job on Jobily.gr by its guid (from search_jobs results): plain-text description, structured sections (responsibilities, experience, must-have skills, education, benefits), how to apply, and the company logo URL. status is 'ok', 'gone' (job closed) or 'not_found'.
| Name | Required | Description | Default |
|---|---|---|---|
| guid | Yes | The job's GUID, e.g. from a search_jobs result. | |
| language | No | Language of the structured sections: 'en' (default) or 'el' (Greek). | en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation is readOnlyHint and idempotentHint, which establish the safety profile. The description adds semantic behavior beyond the annotations: status outcomes ('ok', 'gone', 'not_found'), returned fields, and structured data. No contradict with annotation.
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 about 4 sentences, dense and structured. It front-loads the purpose and enumerates return fields and status. It does not waste words. The only extras are statuses, sections, and URL meaning, which are all necessary.
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?
The tool has 2 params, many sibling tools, and no output schema. In return, the description describes output fields and statuses. The sequence of how it's all connected is given by citing 'from the search_jobs output' and full explanation of result. The system is enough for of simple complexity and small number of parameters.
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?
The input schema is fully documented (100% coverage) and includes a required guid + optional language with default. The written parameter descriptions ('The job guid, e.g. from a search_jobs result.') and language choices ('en'/'el') add slight context beyond the schema, but do not raise the parameter semantics to a simpler level.
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 names a specific verb+resource ('Get the full posting of a job on Job.gr by its guid') and delineates the complete returned payload: plain-text description, structured sections (responsibilities, will-requirements, education), how to apply, company logo, and status values ('ok', 'gone', 'not_found'). This clearly differentiates it from sibling tools like search_jobs.
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 clearly indicates the input source: 'guid (from a search_jobs result)', which implies use after searching. It also explains the 'language' option. It does not explicitly name alternative tools or state 'use get_job when you need full details, search_jobs to search', but the context and the explicit 'from search results' are adequate, if not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sectorsList sectorsAList the job sectors of Jobily.gr with the slugs accepted by search_jobs' sector filter.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, establishing a safe, read-only operation. The description adds value by revealing that the output directly maps to the accepted values for search_jobs' sector filter, which is critical integration knowledge not present in the annotations.
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, well-structured sentence that front-loads the core action and provides just enough integration detail. Every word earns its place, with no redundancy.
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?
For a zero-parameter, read-only list operation with no output schema, the description is completely sufficient. It covers what the tool does and its relationship to search_jobs, giving an agent all necessary information to decide when to invoke it.
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?
The tool has zero parameters, so the schema fully covers parameter information. According to the rubric, the baseline for 0 params is 4, and no additional parameter documentation is needed.
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 lists job sectors of Jobily.gr and specifies that these are the slugs accepted by search_jobs' sector filter. This specificity differentiates it from sibling tools like search_jobs and other lookup tools, making the purpose unambiguous.
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 this tool: when you need sector slugs for search_jobs' sector filter. It provides clear context about its integration point, though it does not explicitly state alternatives or conditions for non-use. The sibling list and context suffice for an agent to infer the typical use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_locationsLook up locationsAList Greek locations (regions and cities) with the slugs accepted by search_jobs' location filter. Pass a query to narrow by name — it is typo-tolerant and accepts Greek, English and greeklish (e.g. 'thesaloniki', 'αθηνα', 'chania'); the full tree is large.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional fuzzy filter matched against location names, Greek/English/greeklish, e.g. 'athens', 'κρήτη', 'thesaloniki'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral details: typo tolerance, support for Greek/English/greeklish, and the large full tree, which helps set expectations about input flexibility and output volume.
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 two sentences with the core purpose front-loaded. It packs essential details (language support, examples, typo tolerance, large tree) without redundancy or filler.
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?
For a simple lookup tool with one optional parameter, full schema coverage, and read-only annotations, the description provides enough context for selection and invocation. It lacks an explicit statement of the return structure or pagination behavior, but that is somewhat mitigated by the 'slugs accepted' phrasing and low tool complexity.
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?
The schema description covers 100% of the single parameter, already stating it is an optional fuzzy filter matched against Greek/English/greeklish names with examples. The tool description largely repeats this information, so it adds minimal value 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 opens with a specific verb and resource: 'List Greek locations (regions and cities)'. It further clarifies its purpose by stating these are 'the slugs accepted by search_jobs' location filter', which clearly distinguishes it from sibling lookup tools like lookup_roles_and_companies.
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 usage context: pass a query to narrow by name, and warns that 'the full tree is large'. It implicitly ties usage to preparing filters for search_jobs, but does not explicitly state when not to use it or mention alternatives beyond the implied search_jobs integration.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_roles_and_companiesLook up roles and companiesATypeahead over job roles and companies on Jobily.gr. Returns up to 7 role and 7 company suggestions whose slugs feed the role/company filters of search_jobs. Works with Greek, English and greeklish input (min 2 characters).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Partial role or company name, Greek or English (min 2 characters), e.g. 'προγραμ', 'account'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful behavioral context: returns up to 7 role and 7 company suggestions, supports Greek/English/greeklish, enforces a 2-character minimum, and explains that the returned slugs feed search_jobs filters. This goes well beyond the annotations.
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?
Three short sentences, front-loaded with the core purpose, and every sentence adds useful information: what it returns, how it integrates with search_jobs, and input constraints. No wasted words.
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?
For a simple one-parameter typeahead tool with strong annotations and full schema coverage, the description is complete: it covers purpose, return limits, language support, minimum input length, and downstream usage. No output schema is needed for this level of simplicity.
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 description coverage is 100%, and the schema already documents the query parameter with min characters and language examples. The description adds 'greeklish' and the downstream slug purpose, but this is marginal value over the schema, so the baseline 3 is appropriate.
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 uses a specific verb and resource: 'Typeahead over job roles and companies on Jobily.gr.' It clearly distinguishes this from sibling tools like lookup_locations and search_jobs by stating it returns role/company suggestions whose slugs feed search_jobs filters.
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 it: before search_jobs, to obtain slugs for role/company filters. It also gives input constraints (min 2 characters, Greek/English/greeklish). However, it does not explicitly state when not to use it or name alternatives like lookup_locations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsSearch jobsASearch job listings on Jobily.gr, the Greek job board. Filter by free-text term, role/location/sector slugs, company, employment type, workplace type and work time. Use lookup_roles_and_companies, lookup_locations and list_sectors to discover valid slugs — unrecognized slug filters are reported in matchedCriteria.unrecognizedTerms and ignored by the search. Returns up to 20 jobs per page with the total count, facet counts (first page) and recovery suggestions when nothing matches. Search results do not include job descriptions — call get_job with a result's guid for the full posting. When total is large (100+) and few filters are active, recommend the user narrow their search using the facets or lookup tools rather than paginating through hundreds of results.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number, 1-indexed (default 1). Each page returns up to 20 jobs. | |
| role | No | Role slug, e.g. 'software-engineer'. Discover via lookup_roles_and_companies. | |
| sector | No | Sector slug, e.g. 'information-technology-it'. Discover via list_sectors. | |
| company | No | Company slug (from lookup_roles_and_companies) or company GUID. | |
| location | No | Location slug, e.g. 'athens', 'thessaloniki'. Discover via lookup_locations. Omit for all of Greece. | |
| searchTerm | No | Free-text search term, Greek or English (e.g. 'πωλητής', 'software engineer'). Prefer the role filter when a role slug is known. | |
| workTimeType | No | One of: full-time, part-time. | |
| workplaceType | No | One of: remote, hybrid, onsite. | |
| employmentType | No | One of: permanent, contract, internship, seasonal, volunteer. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, and the description adds substantial behavioral detail beyond them: unrecognized slug filters are ignored and reported, pages are limited to 20 jobs, facet counts appear on the first page, recovery suggestions are available, and job descriptions are omitted from results. No contradiction with annotations exists.
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?
While the description is long, every sentence earns its place: purpose, filter dimensions, slug discovery, pagination/facets, absence of descriptions, and large-result guidance. It is front-loaded with the core action and resource, and the density is justified by the tool's complexity.
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?
With no output schema, the description adequately explains return characteristics: page size, total count, first-page facet counts, recovery suggestions, and the need to call get_job for full descriptions. It also covers multi-tool coordination and search-narrowing guidance, making the tool fully usable without external docs.
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?
The input schema already fully documents all 9 parameters with 100% coverage, so the baseline is 3. The description adds extra semantic value by explaining that unrecognized slugs are ignored and reported in matchedCriteria.unrecognizedTerms, and by recommending the role filter over free-text search when a role slug is known.
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 opens with a specific verb and resource: 'Search job listings on Jobily.gr'. It enumerates the filtering dimensions and distinguishes itself from siblings by noting that get_job retrieves full postings and lookup tools discover valid slugs.
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 explicitly directs agents to lookup_roles_and_companies, lookup_locations, and list_sectors for discovering valid slugs. It additionally states when to call get_job instead, and when pagination is inappropriate due to large result sets.
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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