naija-faker-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_personA | Generates one basic composite person record with identity and contact fields. Use atomic tools when you need only one attribute. Accepts optional language (hausa, igbo, or yoruba) and gender (male or female). Returns one synthetic object. |
| generate_peopleA | Generates an array of basic composite synthetic person records. Accepts an optional positive count and returns 10 records by default. |
| generate_consistent_personA | Generates one synthetic person whose identity, address, state, and LGA are geographically coherent. The title is filtered to match the ethnic group. Also returns the language and region the record was drawn from. Accepts optional language and gender. |
| generate_consistent_peopleA | Generates an array of synthetic people with geographically coherent identity, address, state, and LGA fields. Accepts an optional positive count and returns 10 records by default. |
| generate_detailed_personA | Generates one synthetic detailed person, including geographically consistent identity data plus health, financial, kin, education, work, and vehicle records. Every field is derived from one identity, so graduation follows birth and employment follows graduation. Defaults to ages 22-65; widen the range with minAge and maxAge, and education is null for anyone too young to have finished a qualification. |
| generate_detailed_peopleA | Generates an array of synthetic detailed people. Each record includes geographically consistent identity data plus health, financial, kin, education, work, and vehicle records. Returns one record by default. Defaults to ages 22-65; widen the range with minAge and maxAge. |
| generate_titleB | Generates a synthetic Nigerian title. |
| generate_nameB | Generates a synthetic Nigerian name. |
| generate_phone_numberB | Generates a synthetic Nigerian phone number. |
| generate_emailB | Generates a synthetic email address. |
| generate_addressA | Generates a synthetic Nigerian address. Pass a region to place it in that part of the country. |
| generate_bvnA | Generates a synthetic BVN for test data only. |
| generate_ninA | Generates a synthetic NIN for test data only. |
| generate_vehicle_recordB | Generates a synthetic Nigerian vehicle record. |
| generate_license_plateC | Generates a synthetic Nigerian license plate. |
| generate_companyA | Generates a synthetic Nigerian company record. |
| generate_universityA | Generates a synthetic Nigerian university record. |
| generate_education_recordA | Generates a synthetic education record. Pass an age and the degree is one the person lived long enough to earn, with a course that fits the degree's discipline. Returns null when the age is too young to have finished a qualification. |
| generate_work_recordA | Generates a synthetic work record. Pass an age and graduation year and the job starts after the degree, with seniority, position, and salary band following years of experience. |
| generate_statesA | Returns the available Nigerian states. |
| generate_lgasA | Returns the available Nigerian LGAs. |
| export_recordsA | Generates a batch of synthetic person records as a single JSON or CSV payload. Use this instead of repeated single-record calls when producing a dataset or fixture file. Nested fields are flattened in CSV. |
| generate_date_of_birthB | Generates a synthetic date of birth and age. |
| generate_marital_statusA | Generates a synthetic marital status. Pass an age to rule out statuses implausible for it. |
| generate_blood_groupA | Generates a synthetic blood group. |
| generate_genotypeA | Generates a synthetic genotype. |
| generate_salaryB | Generates a synthetic salary record. |
| generate_next_of_kinB | Generates a synthetic next-of-kin record. |
| generate_bank_accountA | Generates a synthetic bank account for test data. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| generate_person | Generates a fake person data using naija-faker tool |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| package-docs | Package documentation |
TDQS
Scored across 29 tools
Tools like generate_person, generate_consistent_person, and generate_detailed_person have overlapping purposes (all generate a person), though descriptions clarify differences (basic, coherent, detailed). Similarly, generate_people, generate_consistent_people, and generate_detailed_people are distinguishable by their descriptions, but the overlap could cause confusion when selecting the right tool. generate_states and generate_lgas are clearly distinct, but the line between generate_people and generate_consistent_people might be ambiguous.
The vast majority of tools follow a consistent 'generate_' prefix pattern, with some 'export_records' as an exception. The naming is very predictable: generate_<entity> or generate_<attribute>. The only outlier is export_records, which breaks the pattern but is still clear. Minor deviations include the lack of a consistent verb for retrieval (generate vs export), but overall the pattern is strong.
With 29 tools, the server is on the heavier side, but the scope (synthetic Nigerian data generation) justifies many atomic generators for various attributes. However, the presence of both single and plural versions (person/people) and multiple levels of person generation (basic, consistent, detailed) adds redundancy, making the count feel slightly excessive. It borders on 'too many' but remains within a usable range for a specialized data generation server.
The server covers a wide range of data types: identity (name, email, phone, BVN, NIN), demographics (gender, age, marital status), health (blood group, genotype), education, work, address, vehicle, company, and more. It also provides composite generators for full records. Missing operations include updating or deleting records, but for a synthetic data generator, creation is the core function. The set seems complete for its purpose of generating various synthetic records, with minor gaps like no explicit generator for a 'next of kin' with full details (only as part of detailed person).